DVA-C02 AWS Certified Developer - Associate Questions and Answers

To reduce the time it takes for new Auto Scaling instances to become ready, the most effective optimization is to bake the time-consuming installation and configuration steps into a custom AMI. By creating a prebuilt AMI that already contains the application binaries, dependencies, and baseline configuration, new instances start from an image that is already near “ready to serve.” This minimizes lengthy bootstrapping and significantly shortens the time required to pass ALB health checks and enter service. Therefore, creating a prebuilt AMI (B) is a primary performance optimization for faster scaling events and lower deployment variability.

After creating the AMI, the Auto Scaling group must actually use it. Because the infrastructure is managed with AWS CloudFormation, the developer should update the launch template resource (or launch configuration equivalent) in the CloudFormation template to reference the new AMI ID. This ensures that all newly launched instances use the optimized image consistently and that the change is tracked and repeatable as part of infrastructure as code. That is option C.

The other options are less optimal for the stated goal. Installing and configuring the app at launch time via Systems Manager Run Command (D) or CloudFormation helper scripts (E) still performs the expensive work during instance initialization, which prolongs launch time and can create variance (for example, dependency download time, repo availability, or transient network slowness). Option A (Marketplace AMI) could help only if a suitable prebuilt image exists for the exact custom application—which is unlikely for a proprietary application—and it also reduces control over the build and hardening process compared to maintaining an internal golden AMI.

Therefore, the best combination is B (bake the app into a custom AMI) and C (update the CloudFormation-managed launch template to use that AMI).

This solution allows the fastest response for the query because it enables the query to use a single partition key value (the Product ID) and a range of sort key values (the Product Rating) to find the matching items. A global secondary index (GSI) is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI can be created at any time and can be queried or scanned independently of the base table. A local secondary index (LSI) is an index that has the same partition key as the base table, but a different sort key. An LSI can only be created when the base table is created and must be queried together with the base table partition key. Using a GSI with Product ID as the partition key and Review ID as the sort key will not allow the query to use a range of sort key values to find the highest ratings. Using an LSI with Product ID as the partition key and Product Rating as the sort key will not work because Product ID is not the partition key of the base table. Using an LSI with Review ID as the partition key and Product ID as the sort key will not allow the query to use a single partition key value to find the matching items.

https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html

An SQS FIFO queue is a type of queue that preserves the order of messages and ensures that each message is delivered and processed only once1. This is suitable for the scenario where the developer wants to ensure that there are no out-of-order updates in the legacy system.

The visibility timeout value is the amount of time that a message is invisible in the queue after a consumer receives it2. This prevents other consumers from processing the same message simultaneously. If the consumer does not delete the message before the visibility timeout expires, the message becomes visible again and another consumer can receive it2.

In this scenario, the developer needs to configure the visibility timeout value to be longer than the maximum processing time of the legacy system, which is 5 minutes. This will ensure that the message remains invisible in the queue until the legacy system finishes processing it and deletes it. This will prevent duplicate or out-of-order processing of messages by the legacy system.

This solution allows the developer to keep the dependencies of the Lambda functions up to date with the least additional complexity because it eliminates the need to update each function individually. A Lambda layer is a ZIP archive that contains libraries, custom runtimes, or other dependencies. The developer can create a layer that contains all of the shared dependencies and attach it to multiple Lambda functions. When the developer updates the layer, all of the functions that use the layer will have access to the latest version of the dependencies.

DynamoDB already supports encryption at rest by default (with AWS owned keys or AWS managed/customer managed KMS keys). However, the requirement here is stronger and explicit: patient data must be encrypted before it is stored in DynamoDB, meaning the encryption must occur client-side / application-side so that the data remains encrypted as it traverses the stack and is still encrypted when written to the table. This is often required for highly sensitive PII/PHI where administrators or downstream systems should not see plaintext.

Option A meets this requirement by encrypting the payload inside the Lambda function before making the PutItem call to DynamoDB, using KMS-backed envelope encryption via the AWS Encryption SDK (the option names it “Database Encryption SDK,” but the intent is client-side encryption). The encrypted fields remain ciphertext in transit to DynamoDB and at rest inside DynamoDB, satisfying both “in transit” and “at rest” with encryption happening prior to storage.

Option B only ensures DynamoDB encryption at rest at the service level, but the data would still be plaintext in the item attributes as seen by the application and anyone with DynamoDB read permissions (even though stored encrypted on disk). It does not guarantee “encrypted before stored.”

Option C encrypting after the write via streams is too late: plaintext would already be stored (and potentially accessed) before post-processing.

Option D adds unnecessary workflow complexity. Encrypting in a queue still requires encryption before DynamoDB, but it does not inherently simplify compared to encrypting directly in the Lambda that writes to DynamoDB.

Therefore, client-side encryption in Lambda using KMS-backed encryption is the correct solution.

https://docs.aws.amazon.com/lambda/latest/dg/nodejs-context.html https://docs.aws.amazon.com/lambda/latest/dg/nodejs-logging.html

There is no explicit information for the runtime, the code is written in Node.js.

AWS Lambda is a service that lets developers run code without provisioning or managing servers. The developer can use the AWS request ID field in the context object to obtain a unique identifier for each function invocation. The developer can configure the application to write logs to standard output, which will be captured by Amazon CloudWatch Logs. This solution will meet the requirement of logging key events with a unique identifier.

AWS Lambda automatically integrates with Amazon CloudWatch Logs, but logging only works if the function’s execution role has the correct IAM permissions. AWS documentation states that Lambda functions require permissions for logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents to successfully publish log entries.

In this scenario, CloudWatch metrics show errors, which confirms that the function is executing. The absence of logs despite logging statements in the code strongly indicates a missing IAM permission issue, not a runtime or tracing issue.

CloudWatch Lambda Insights (Option B) provides performance metrics but does not enable basic logging. AWS X-Ray (Option C) provides distributed tracing but does not replace CloudWatch Logs. Option D is invalid because CloudWatch does not assume roles to collect logs.

AWS documentation explicitly identifies missing log permissions as the most common cause of absent Lambda logs. Therefore, granting the Lambda execution role the appropriate CloudWatch Logs permissions resolves the issue.

AWS Lambda layers are specifically designed to share common code and dependencies across multiple Lambda functions. A layer is a ZIP archive that contains libraries, a custom runtime, or other function dependencies. Layers are versioned, so the developer can publish a new layer version when libraries change and then update functions to reference the new version. This directly meets the requirements to centralize libraries, update conveniently, and keep libraries versioned—while requiring minimal development work.

Using a single shared layer significantly reduces duplication across function deployment packages. It also simplifies CI/CD because the developer can build and publish the layer independently and reuse it across many functions.

Option A (CodeArtifact) is useful for dependency management, but it still requires each Lambda deployment to package dependencies or download them during build. It’s more moving parts than a layer for this use case.

Option B (container images) can centralize dependencies but typically requires more effort: building, scanning, versioning container images, and updating function image URIs. It’s heavier operationally than layers for “shared custom libraries.”

Option D (EFS) can be mounted by Lambda, but introduces networking considerations (VPC config), EFS lifecycle/permissions, and is unnecessary for simply sharing libraries. Also, cold start and operational overhead can increase.

Therefore, creating a Lambda layer for the shared libraries is the least-effort, most standard approach.

This is a classic cross-account S3 access requirement: EC2 instances in Account A must read from a private S3 bucket in Account B without making the bucket public.

The correct pattern is to grant access using IAM + S3 bucket policy while keeping the bucket private. In Account B, you create an IAM role that has the necessary S3 read permissions (such as s3:GetObject and potentially s3:ListBucket depending on access needs). That is Option A.

However, permissions on the role alone are not sufficient, because the S3 bucket is in a different account and is private by default. AWS requires that the bucket owner explicitly grants access to the external principal. This is done by adding a bucket policy in Account B that allows the principal (either the role in Account A or a role session via STS) to access the bucket objects. That is Option D.

Option B is not correct in the “select two” sense because adding permissions to Account A’s instance profile role does not, by itself, grant access to a bucket owned by Account B. You still need the bucket policy or another resource-based policy from the bucket owner.

Option C violates the requirement (“without exposing the S3 bucket to anyone else”). Making the bucket public is unnecessary and insecure.

Option E is incorrect because a trust policy controls who can assume a role (sts:AssumeRole), not what S3 permissions the role has. Also, trust policies do not grant s3:Get* access.

Therefore, create an IAM role with S3 read permissions in Account B and grant access via the Account B bucket policy.

The correct answer is A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment.

A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment. This is correct. This solution will meet the requirements with the least development effort, because it uses a feature of the AWS SAM CLI that supports a project-level configuration file that can be used to configure AWS SAM CLI command parameter values1. The configuration file can have multiple environments, each with its own set of parameter values, such as stack name, region, capabilities, and more2. The developer can use the --config-env option to specify which environment to use when deploying the application3. This way, the developer can avoid creating multiple templates or scripts, or manually overriding parameters for each environment.

B. Create additional AWS SAM templates for each testing and staging environment. Write a custom shell script that uses the sam deploy command and the --template-file flag to deploy updates to the environments. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires creating and maintaining multiple templates and scripts for each environment. This can introduce duplication, inconsistency, and complexity in the deployment process.

C. Create one AWS SAM configuration file that has default parameters. Perform updates to the testing and staging environments by using the —parameter-overrides flag in the AWS SAM CLI and the parameters that the updates will override. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires manually specifying and overriding parameters for each environment every time the developer deploys the application. This can be error-prone, tedious, and inefficient.

D. Use the existing AWS SAM template. Add additional parameters to configure specific attributes for the serverless function and database table resources that are in each environment. Deploy updates to the testing and staging environments by using the sam deploy command. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires modifying the existing template and adding complexity to the resource definitions for each environment. This can also make it difficult to manage and track changes across different environments.

Tracing Distributed Systems: AWS X-Ray is designed to trace requests across services, helping identify bottlenecks in distributed applications like this one.

No Code Changes: Enabling X-Ray tracing often requires minimal code changes, meeting the requirement.

Identifying Bottlenecks: Analyzing X-Ray traces and logs will reveal latency in communications between different AWS services, leading to the high duration time.

Comprehensive and Detailed Step-by-Step Explanation:

Issue Analysis:

The stream uses service name as the partition key. This can cause "hot partition" issues when a few service names generate significantly more logs compared to others, causing uneven distribution of data across shards.

Metrics show that the write capacity used is below provisioned capacity, which confirms that the throughput errors are due to shard-level limits and not overall capacity.

Option C: Change Partition Key to Creation Timestamp:

By changing the partition key to the creation timestamp (or a composite key including timestamp), the distribution of data across shards can be randomized, ensuring an even spread of records.

This resolves the shard overutilization issue and eliminates ProvisionedThroughputExceededException.

Why Other Options Are Incorrect:

Option A: Switching to on-demand capacity mode might temporarily alleviate the issue, but the root cause (hot partitioning) remains unresolved.

Option B: Adding shards increases capacity but does not fix the skewed data distribution caused by using the service name as the partition key.

Option D: Creating separate streams for each service adds unnecessary complexity and does not scale well as the number of services grows.

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Environment Isolation: Creating separate secrets for each environment (development, staging, etc.) ensures clear separation and prevents accidental leaks.

Automatic Rotation: Secrets Manager provides built-in rotation capabilities, enhancing security posture.

Versioning: Tracking changes to secrets is essential for auditing and compliance.

IAM Roles for EC2 (A): The most secure way to provide AWS permissions from EC2.

Create a role with a policy allowing s3:GetObject on the specific bucket.

Attach the role to an instance profile and associate that profile with your instances.

Pre-signed URLs (C): Temporary, authenticated URLs for specific S3 actions.

Modify the app to use the AWS SDK to call GeneratePresignedUrl.

Embed these URLs when a user is properly logged in, allowing download access.

Sensitive environment variables in Lambda should be protected using encryption helpers integrated with AWS KMS. Lambda environment variables are encrypted at rest, and when using a customer managed KMS key, teams can control the key policy, access, and rotation for their own microservices—matching the policy requirement that each team has “full control” over key rotation. AWS managed keys do not provide the same level of customer control over lifecycle and rotation decisions.

Therefore, the correct approach is to create customer managed KMS keys (one per team/service boundary or per microservice, depending on governance) and configure each Lambda function to use the relevant CMK for encrypting its environment variables. The Lambda execution role must have permission to use the key for decryption at runtime, typically requiring kms:Decrypt (and sometimes kms:DescribeKey depending on tooling). This is captured by Option B.

Option A and D use AWS managed keys, which do not satisfy the requirement for each team to control rotation. Rotation control (and policy ownership) is a key differentiator of customer managed keys.

Option C is not correct because the Lambda execution role does not need kms:CreateGrant and kms:Encrypt for decrypting environment variables at runtime. Over-permissioning increases risk. The Lambda service handles encryption of environment variables; the function runtime principally needs the ability to decrypt.

Global Secondary Index (GSI): GSIs enable alternative query patterns on a DynamoDB table by using different partition and sort keys.

Addressing Query Bottleneck: By making the slow-query attribute the GSI's partition key, you optimize queries on that attribute.

Scalability: GSIs automatically scale to handle increasing data volumes.

AWS CodeArtifact emits events when repository actions occur, such as publishing a new package. AWS documentation recommends Amazon EventBridge as the native service for routing AWS service events to targets with minimal configuration and no infrastructure to manage.

By creating an EventBridge rule that matches CodeArtifact package publication events and routing those events directly to an Amazon SNS topic, the developer can notify the team immediately. SNS provides built-in email subscriptions, retries, and delivery guarantees without custom code.

Option C is the most operationally efficient because it uses fully managed, serverless services and requires no custom compute, code maintenance, or orchestration. EventBridge directly supports SNS as a target, making the configuration straightforward.

Option A is invalid because CodeArtifact does not directly publish notifications to SNS topics. Options B and D introduce unnecessary complexity by adding Lambda or Step Functions and Amazon SES, increasing operational overhead without added value.

Therefore, using EventBridge to route CodeArtifact events to SNS is the AWS-recommended and simplest solution.

To prove integrity (and authenticity) of data in transit, the correct cryptographic primitive is a digital signature. AWS KMS supports signing and verification operations using asymmetric KMS keys designed for signing (for example, RSA_SIGN_PSS, RSA_SIGN_PKCS1, or ECC_NIST_P256 key specs). The developer signs the data (or more commonly, a hash of the data) using the private key, and the external recipient verifies the signature using the corresponding public key. If the data is modified in transit, signature verification fails, proving the content was changed.

Option C exactly describes this model: sign with the private key, share the public key. The private key remains protected (ideally never leaving KMS). The public key can be distributed safely because it cannot be used to forge signatures.

Option A (encryption) provides confidentiality, not integrity proof to a third party, and sharing a symmetric encryption key is insecure and breaks key management principles.

Option B is incorrect because symmetric keys do not provide non-repudiation and generally require the verifier to possess the same secret key, which would allow the verifier to forge signatures too. While HMAC can validate integrity between trusted parties, it does not meet the typical “prove integrity” requirement to an external party without sharing a secret.

Option D is backward and insecure: you never share a private key.

Therefore, use an asymmetric KMS signing key to sign with the private key and provide the public key for verification.

The requirement is to redact PII before the object content reaches the consumer, while the analytics service continues to use standard S3 GET requests. The most operationally efficient way is Amazon S3 Object Lambda.

S3 Object Lambda lets you add your own code to process and transform data as it is retrieved from S3. Instead of the analytics service reading directly from the bucket, it reads through an S3 Object Lambda Access Point. When the service performs a GET request, S3 invokes the associated Lambda function, which can modify the object payload on the fly—for example, by calling an external or internal PII redaction API and returning a redacted version of the report. The original object remains unchanged in the bucket, while consumers receive the transformed (redacted) response.

This approach is operationally efficient because it avoids:

duplicating and storing redacted copies of every report,

refactoring the analytics service to use a different datastore or ingestion path,

building a separate proxy service layer.

Option A requires significant refactoring and ongoing ETL/warehouse ops.

Option C provides confidentiality via encryption but does not perform redaction (analytics would still see PII after decryption).

Option D changes the access pattern completely (analytics no longer uses GET) and adds messaging complexity without directly returning redacted object content.

Therefore, S3 Object Lambda + Object Lambda Access Point is the most efficient solution to redact data at retrieval time.

AWS Lambda combined with Amazon S3 event notifications provides the most cost-effective and lowest-latency solution for processing objects uploaded to S3. AWS documentation states that S3 event notifications can invoke Lambda functions immediately after object creation, eliminating delays associated with polling or scheduled execution.

Because the data arrives only about 10 times per day and processing completes quickly, Lambda is ideal due to its pay-per-invocation pricing model. There is no need to maintain always-on infrastructure, which significantly reduces cost compared to running EC2 instances. The developer pays only for execution time and requests.

Using CloudWatch alarms (Option A) or scheduled events (Option C) introduces unnecessary latency and complexity. These approaches either delay processing or require periodic checks rather than reacting instantly to object uploads. Polling from EC2 (Option D) is the most expensive and least efficient option because it requires continuously running compute resources.

AWS best practices for event-driven architectures explicitly recommend S3 event notifications → Lambda for near-real-time processing of uploaded data. This approach minimizes operational overhead, achieves near-zero idle cost, and provides the fastest possible response to new data.

Therefore, invoking a Lambda function directly through an S3 event notification is the optimal solution.

By default, an Auto Scaling group uses EC2 status checks to determine instance health. EC2 status checks confirm that an instance is running and reachable at the infrastructure level, but they do not validate that the application is healthy behind the load balancer. In this scenario, the ALB is returning HTTP 502 errors and the target group shows instances as unhealthy, meaning the application is failing health checks (or not responding correctly). However, because the Auto Scaling group is using the default EC2 health check type, it does not consider these instances unhealthy for replacement.

AWS recommends configuring the Auto Scaling group to use ELB health checks when instances serve traffic behind an ALB or NLB. With ELB health checks enabled, Auto Scaling evaluates target health as reported by the load balancer target group. If the ALB marks an instance unhealthy, the Auto Scaling group will treat that instance as unhealthy and terminate and replace it automatically, restoring capacity with healthy targets.

Why the other options are insufficient:

Cooldown period (A) affects scaling timing, not health replacement logic.

Lifecycle hook changes (B) can help with initialization timing, but they do not cause Auto Scaling to replace instances based on ALB target health unless ELB health checks are enabled.

Health check grace period (D) delays health evaluation after launch, which can reduce premature replacement, but it still won’t replace instances based on ALB target group status unless the health check type is ELB.

Therefore, switching the Auto Scaling group health check type to Elastic Load Balancing (ELB) is the correct fix.

Reserved concurrency guarantees a specific number of concurrent executions for a critical Lambda function. This ensures that the critical function always has sufficient resources to execute, even if other functions are consuming concurrency.

Why Option A:

Ensures Function Availability: Reserved concurrency isolates the critical Lambda function from other functions.

Low Overhead: Configuring reserved concurrency is straightforward and requires minimal setup.

Why Not Other Options:

Option B: Provisioned concurrency is ideal for reducing cold starts, not for managing execution limits.

Option C & D: Alarms and re-invocation mechanisms add complexity without resolving the root cause.

Managing Concurrency for AWS Lambda

AWS CloudFormation is a service that allows developers to model and provision AWS resources using templates. A CloudFormation template can define the load test resources, such as EC2 instances, load balancers, and Auto Scaling groups. A CloudFormation stack set is a collection of stacks that can be created and managed from a single template in multiple Regions and accounts. The AWS CLI create-stack-set command can be used to create a stack set from a template and specify the Regions where the stacks should be created. Reference: Working with AWS CloudFormation stack sets

For cross-account access from EC2 in Account A to a Kinesis data stream in Account B, the recommended secure pattern is to use STS AssumeRole into a role in the resource-owning account (Account B). This ensures Account B retains control over what permissions are granted to external principals and allows Account A workloads to obtain temporary credentials scoped to the required actions.

First, create an IAM role in Account B that has the necessary Kinesis read permissions (such as kinesis:GetRecords, kinesis:GetShardIterator, kinesis:DescribeStream, and related read actions as required). That corresponds to option B. This role represents the permission boundary controlled by Account B for accessing its stream.

Second, configure the role’s trust policy in Account B to allow the instance profile role (or another IAM principal) from Account A to assume it. In practice, the trust relationship is defined on the Account B role and specifies the Account A role as the trusted principal, enabling sts:AssumeRole. This corresponds to option C (the intent is “allow the instance profile role to assume the IAM role in Account B”).

Option A alone is insufficient because permissions in Account A do not grant access to resources owned by Account B without an explicit cross-account authorization path. Option D is incorrect because trust policies do not “allow reads from the stream”; they allow principals to assume roles, and permissions policies allow service actions. Option E (resource-based policy) is not the primary mechanism for Kinesis Data Streams cross-account access in this scenario compared with the standard role assumption model; the secure and common approach is to assume a role in the owning account.

Therefore, the correct actions are B (create the role with read permissions in Account B) and C (configure trust to allow Account A’s instance role to assume it).

In AWS Lambda, CPU power scales proportionally with the memory configuration. Lambda does not let you directly set “CPU cores” as a standalone setting in the general case; instead, increasing the function’s configured memory increases the CPU allocation (and other resources such as network throughput) available to the function. Therefore, to reduce latency caused by insufficient CPU, the developer should increase the function’s memory setting.

Option B directly addresses the CPU limitation in the supported Lambda configuration model. This is a common performance tuning approach: raise memory, benchmark, and find the optimal cost/performance point.

Option A is not the right lever for most Lambda functions because Lambda compute is configured via memory (and architecture), not by directly raising a “vCPU quota” per function in a typical tuning workflow.

Option C increases /tmp storage capacity and helps when dealing with large temporary files, not CPU availability.

Option D increases the maximum runtime allowed per invocation, but it does not give the function more CPU and will not reduce latency caused by CPU starvation.

Therefore, increasing the allocated memory is the correct way to increase CPU for a Lambda function.

IAM instance profiles are containers for IAM roles that can be associated with EC2 instances. An IAM role is a set of permissions that grant access to AWS resources. An IAM role can be used to allow an EC2 instance to access an S3 bucket by including the appropriate permissions in the role’s policy. The S3:ListBucket permission allows listing the objects in an S3 bucket. By updating the IAM instance profile with this permission, the application on the EC2 instance can retrieve the objects from the S3 bucket and display them to the user. Reference: Using an IAM role to grant permissions to applications running on Amazon EC2 instances

The solution that will meet the requirements is to write data to Amazon ElastiCache. This way, the application can write session data to a fast, scalable, and reliable in-memory data store that can be served reliably across multiple requests. The other options either involve writing data to persistent storage, which is slower and more expensive than in-memory storage, or writing data to the root filesystem, which is not shared among multiple EC2 instances.

CloudFront is a content delivery network that caches objects at edge locations to reduce latency and origin load. When the developer updates static artifacts in the origin S3 bucket, CloudFront may continue serving cached versions until the objects expire based on cache-control headers or the distribution’s TTL settings. That is why the developer sees the updated files in S3 but not on the site.

The standard fix is to perform a CloudFront cache invalidation for the updated object paths (for example, /app.js, /styles.css, or /* if needed). An invalidation forces CloudFront edge locations to remove the cached objects so the next viewer request fetches the latest version from the S3 origin.

Option C directly addresses this behavior and is the correct operational practice when cache TTLs would otherwise delay updates.

Option A (Object Lock) is for WORM retention/compliance and has nothing to do with CloudFront cache refresh.

Option B might change what exists in S3 but does not guarantee CloudFront will stop serving cached content; the cache can still serve objects even if deleted from the origin (until it revalidates/refreshes).

Option D is unnecessary; the origin does not need to change to fetch updated content.

Therefore, invalidate the CloudFront cache after deployment to ensure the new artifacts are served.

AWS CodeBuild supports parallel test execution through batch builds and multiple compute environments. This feature allows a single CodeBuild project to divide test workloads across multiple isolated environments that run concurrently. Each environment executes a subset of the test suite, significantly reducing total build time.

AWS documentation emphasizes using native CodeBuild capabilities to minimize operational complexity. Parallel execution eliminates the need to manually manage infrastructure, test orchestration logic, or EC2 fleets. CodeBuild automatically provisions and terminates build environments, scales as needed, and integrates seamlessly with CI/CD pipelines.

Options A and D require manual coordination and infrastructure management, increasing operational overhead. Option C does not address the performance problem.

Therefore, using CodeBuild’s parallel compute environments is the most efficient and AWS-recommended approach.

The solution that will meet the requirements is to create an Amazon EventBridge rule that runs on a regular schedule to invoke the Lambda function. This way, the developer can use an automated and serverless way to invoke the function every 10 minutes. The developer can also use a cron expression or a rate expression to specify the schedule for the rule. The other options either involve using an Amazon EC2 instance, which is not serverless, or using environment variables or query parameters, which do not trigger the function.

When IAM user credentials require MFA for API access, the correct approach is to obtain temporary security credentials from AWS Security Token Service (STS) that are validated with an MFA code. AWS documentation describes using STS to issue temporary credentials that applications can use instead of long-term access keys, especially when MFA is required.

The specific STS API operation used for an IAM user to obtain temporary credentials is GetSessionToken. This call supports MFA by accepting the user’s MFA device serial number and a time-based one-time password (TOTP) code. STS then returns a set of temporary credentials: AccessKeyId, SecretAccessKey, and SessionToken, which the SDK can use to sign subsequent API requests. This is the standard method for enabling MFA-protected API access for IAM users.

Why the other options are wrong:

GetFederationToken is used to obtain temporary credentials for a federated user, often for scenarios where you want to grant access to resources for users who do not have IAM users. It’s not the typical method for IAM-user MFA enforcement for all calls.

GetCallerIdentity simply returns identity details for the current credentials; it does not generate credentials.

DecodeAuthorizationMessage is used to decode encoded authorization failure messages returned by AWS, not to authenticate.

Therefore, to access an API protected by MFA requirements for an IAM user, the developer should call GetSessionToken and then use the returned temporary credentials in the AWS SDK.

Amazon API Gateway is a service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. The developer can define a development stage on the API Gateway API and instruct the other developers to point the endpoints to the development stage. This way, the developer can provide beta access to the new version of the API without affecting customers who use the production stage. This solution will meet the requirements with the least operational overhead.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS-doc-aligned):

The most operationally efficient way to share and patch common dependencies across multiple Lambda functions is to use AWS Lambda layers. A Lambda layer is an independently managed deployment artifact that can contain libraries and other dependencies. Multiple functions can reference the same layer, which avoids packaging identical open source modules into each function bundle.

When a vulnerability or bug is found in a shared module, you patch it once by publishing a new layer version that contains the updated dependencies. Then you update each Lambda function configuration to reference the new layer version. This centralizes dependency management, reduces duplicated artifacts, and makes rollouts and rollbacks cleaner (you can switch layer versions quickly). It also keeps function deployment packages smaller, which often improves deployment speed and can reduce cold-start overhead associated with larger bundles.

The other options introduce unnecessary operational burden:

Downloading modules at cold start (CloudFront/S3) increases latency and adds failure modes at runtime.

Hosting a private registry on EC2 requires patching, scaling, availability management, and security hardening—high operational overhead.

A CloudFormation registry extension and daily scans are not a standard or efficient mechanism for packaging runtime Node.js dependencies into Lambda functions.

Lambda layers are a documented AWS-native feature intended for precisely this use case: sharing common code and libraries across functions while enabling controlled versioning. Therefore, creating a layer for the open source modules and updating the functions to consume the layer is the most efficient and maintainable approach.

Why Option C is Correct:Amazon Verified Permissions provides fine-grained access control capabilities, making it ideal for managing complex user permissions. It integrates with OIDC IdPs for authentication and allows applications to request authorization decisions dynamically. It is also easily adaptable to newer applications.

Why Other Options are Incorrect:

Option A: Cognito identity pools do not natively support fine-grained permission analysis or management.

Option B: Managing permissions in application logic adds significant operational overhead.

Option D: IAM roles are not designed for application-specific fine-grained access control and are more suitable for resource-level permissions.

AWS Documentation References:

Amazon Verified Permissions

To monitor memory usage in Amazon Elastic Beanstalk environments, it's important to understand that default Elastic Beanstalk monitoring capabilities in Amazon CloudWatch do not track memory usage, as memory metrics are not collected by default. Instead, the Amazon CloudWatch agent must be configured to collect memory usage metrics.

Why Option C is Correct:

The Amazon CloudWatch agent can be installed and configured to monitor system-level metrics such as memory and disk utilization.

To enable memory tracking, developers need to install the CloudWatch agent on the Amazon Elastic Compute Cloud (EC2) instances associated with the Elastic Beanstalk environment.

After installation, the agent can be configured to collect memory metrics, which can then be sent to CloudWatch for further analysis.

How to Implement This Solution:

Install the CloudWatch Agent:Use .ebextensions or AWS Systems Manager to install and configure the CloudWatch agent on the EC2 instances running in the Elastic Beanstalk environment.

Modify CloudWatch Agent Configuration:Create a config.json file that specifies memory usage tracking and other desired metrics.

Enable Metrics Reporting:The CloudWatch agent can push the metrics to CloudWatch, where they can be monitored.

Why Other Options are Incorrect:

Option A: Configuring the agent to push logs is not sufficient to track memory metrics. This option addresses logging but not system-level metrics like memory usage.

Option B: The .ebextensions directory is used to customize Elastic Beanstalk environments but does not directly track memory metrics without additional configuration of the CloudWatch agent.

Option D: Configuring a CloudWatch dashboard will only visualize the metrics that are already being collected. It will not enable memory usage tracking.

AWS Documentation References:

Amazon CloudWatch Agent Overview

Elastic Beanstalk Customization Using .ebextensions

Monitoring Custom Metrics

This solution will ensure that the repository package’s unit tests run in the new deployment environment with the least overhead because it uses AWS CodeBuild to build and test the code in a fully managed service, and AWS CodePipeline to orchestrate the deployment stages and actions. Option A is not optimal because it will use AWS CodeCommit instead of AWS CodeBuild, which is a source control service, not a build and test service. Option C is not optimal because it will use AWS CodeDeploy instead of AWS CodeBuild, which is a deployment service, not a build and test service. Option D is not optimal because it will add an action to the source stage instead of creating a new stage, which will not follow the best practice of separating different deployment phases.

Amazon API Gateway supports multiple stages, allowing developers to deploy and test different versions of an API independently. By creating a new test stage, the developer can route traffic to an updated Lambda function without impacting production users.

AWS documentation recommends using stage variables to dynamically reference different Lambda function versions or aliases. This approach enables safe testing while reusing the same API configuration.

Canary deployments (Option A) expose a portion of production users to the new code, which violates the requirement. Changing the endpoint type (Option B) disrupts production. Duplicating the entire stack (Option D) introduces unnecessary complexity and overhead.

Using a separate test stage is the most efficient, low-risk, and AWS-recommended solution.

The solution that will meet the requirements is to add a new stage to the pipeline. Use AWS CodeBuild as the provider. Add the new stage before the stage that deploys code revisions to the test environment. Write a buildspec that fails the CodeBuild stage if any test does not pass. Use the test reports feature of CodeBuild to integrate the report with the CodeBuild console. View the test results in CodeBuild. Resolve any issues. This way, the developer can run the unit tests automatically during the CI/CD process and catch any bugs before deploying to the test environment. The developer can also use the test reports feature of CodeBuild to view and analyze the test results in a graphical interface. The other options either involve running the tests manually, running them after deployment, or using a different provider that requires additional configuration and integration.

To provide login functionality with branded sign-in pages and minimal custom code, the best solution is Amazon Cognito Hosted UI. Cognito User Pools include a managed authentication service that can handle sign-up/sign-in, MFA options, password resets, and OAuth/OpenID Connect flows. The Hosted UI provides ready-to-use authentication pages and endpoints, which removes the need to build and maintain custom authentication pages and backend logic.

AWS allows customization of the Hosted UI to match company branding (for example, logo, colors, and CSS styling). This directly meets the requirement that “all pages must match branding” while minimizing development effort. The application can integrate with Cognito using standard authorization flows and tokens, which works well with Lambda-based architectures (for example, API Gateway authorizers with Cognito user pools).

Option A (S3 static pages) would still require implementing authentication flows, session handling, token management, and secure integration with the backend—significantly more custom code.

Option B requires writing and maintaining custom sign-in page serving logic plus authentication handlers, which increases code and operational overhead.

Option C (Lambda@Edge) is even more complex, introducing edge execution and additional deployment/versioning considerations, and still requires custom auth logic.

Therefore, using Amazon Cognito User Pools with the Hosted UI and branding customization is the lowest-code, most maintainable solution.

These solutions will mitigate the error most cost-effectively because they do not require increasing the provisioned throughput of the DynamoDB table or creating additional resources. Exponential backoff is a retry strategy that increases the waiting time between retries to reduce the number of requests sent to DynamoDB. The AWS SDKs for DynamoDB implement exponential backoff by default and also provide other features such as automatic pagination and encryption. Increasing the read and write throughput of the DynamoDB table, creating a DynamoDB Accelerator (DAX) cluster, or creating a second DynamoDB table will incur additional costs and complexity.

Problem: CloudFormation can't find the custom AMI in the target region (us-west-1) because AMIs are region-specific.

Copying AMIs:

AMIs can be copied across regions, maintaining their configuration.

This approach minimizes operational overhead as the existing CloudFormation template can be reused with a minor update.

Updating the Template:

Modify the CloudFormation template in us-west-1 to reference the newly copied AMI's ID in that region.

The requirement has two parts: (1) authenticate users with a third-party SAML IdP, and (2) after authentication, allow those users to access AWS services like Amazon S3 and DynamoDB. The AWS pattern for this is to federate the external identity into AWS and then exchange that identity for temporary AWS credentials.

Amazon Cognito identity pools are designed to provide AWS credentials to authenticated users (via AWS STS) so the users can call AWS services directly or through an application backend. Identity pools support federation with external identity providers, including SAML 2.0. When a user authenticates with the third-party SAML IdP, the identity pool can accept the SAML assertion, map the user to an IAM role, and return temporary, scoped credentials (AccessKeyId/SecretAccessKey/SessionToken) associated with that role. Those credentials can be restricted using IAM policies to only the required S3 buckets, DynamoDB tables, and actions, satisfying least privilege.

Option A is incorrect because a Cognito user pool is primarily a user directory and OIDC/OAuth provider for application authentication; while user pools can integrate with SAML IdPs for federation, user pools alone do not directly issue AWS service credentials. The key requirement here is access to S3/DynamoDB, which is the role of an identity pool. Option C is not appropriate because IAM is not intended to provide end-user sign-up/sign-in for external users; it is for AWS identities and permissions. Option D is unrelated: CloudFront signed URLs control access to CloudFront-distributed content and do not authenticate users with SAML or provide AWS credentials.

Therefore, B meets both requirements: federate SAML authentication through a Cognito identity pool and provide temporary AWS credentials for accessing S3 and DynamoDB.

The requirement emphasizes collaboration, frequent code changes, and deploying from source control with the fewest manual steps. This aligns with a CI/CD pipeline approach where commits or merges automatically trigger builds and deployments. Using AWS CodePipeline orchestrates the stages (source, build, deploy), and AWS CodeBuild performs the build/package steps for the Lambda/serverless application. When integrated with a repository (such as CodeCommit, GitHub, or another supported provider), CodePipeline can automatically start when changes are merged into specific branches, enabling consistent, repeatable deployments.

Option C provides the least operational overhead for teams: developers push code and merge pull requests; the pipeline automatically builds artifacts, runs tests (if configured), packages dependencies, and deploys updates to Lambda (often via AWS SAM/CloudFormation under the hood). This removes manual “build on my laptop” drift, prevents inconsistent deployments between developers, and supports frequent iteration safely.

Option B (SAM CLI from a local machine) requires a developer to manually run build/deploy commands and assumes each developer’s environment is configured correctly. That increases manual steps and the risk of differences across machines. Option D (uploading zip in the console) is highly manual and not suitable for frequent changes or team collaboration. Option A is also suboptimal because storing built artifacts in source control is an anti-pattern; builds should be reproducible and produced by CI, not committed binaries.

Therefore, C is the best choice: set up CodePipeline + CodeBuild so merges to controlled branches automatically trigger builds and deployments to AWS Lambda with minimal manual intervention.

To create a central logging solution for microservices, using Amazon CloudWatch Logs is a recommended and effective approach. Here's why:

Amazon CloudWatch Logs Streams allow you to centralize logs from different services, which is crucial as the number of microservices increases.

Each microservice can have its own dedicated log stream within Amazon CloudWatch Logs, providing clear segregation of logs while still allowing centralized management.

This setup enables developers to monitor, search, and analyze logs efficiently using tools like CloudWatch Insights.

Other options like CloudTrail (B) are designed for API activity monitoring, not application logs. VPC Flow Logs (C) focus on network traffic rather than application behavior. AWS Cloud Map (D) is for service discovery and routing, not logging.

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:

To redact PII from S3 objects before they are accessed by the analytics service, the most efficient solution is to use S3 Object Lambda. S3 Object Lambda allows you to add your own code (Lambda function) to process and transform data when it is retrieved from Amazon S3. You can attach a Lambda function to an S3 Object Lambda Access Point, which in this case would run a redaction API to remove PII from the reports.

Operational Efficiency: S3 Object Lambda handles data processing on the fly, without requiring the data to be permanently transformed or moved to another service (like Amazon Redshift).

Alternatives:

Option A: Loading the data into Amazon Redshift would require refactoring the analytics service and maintaining an additional data pipeline, increasing complexity.

Option C: Using AWS KMS for encryption protects data at rest and in transit, but it does not address PII redaction.

Option D: SNS is a messaging service and does not support direct data transformation.

API Gateway Stage Variables: These are designed for configuring dynamic values for your APIs in different deployment stages (dev, test, prod). Here's how to use them for third-party endpoints:

In the API Gateway console, access the "Stages" section of your API.

For each stage, create a stage variable named something like thirdPartyEndpoint.

Set the value of this variable to the actual endpoint URL for that specific environment.

When configuring API requests within your API Gateway method, reference this endpoint using ${stageVariables.thirdPartyEndpoint}.

Why Stage Variables Excel Here:

Environment Isolation: This approach keeps the endpoint configuration specific to each deployment stage, ensuring the right endpoints are used during development, testing, and production cycles.

Ease of Management: You manage the endpoints directly through the API Gateway console without additional infrastructure.

Using Amazon ElastiCache for Memcached to store and manage the session data will reduce the memory load and improve the performance of the web server. Using Amazon RDS for MySQL DB instance to store the application data will provide a scalable, reliable, and managed database service. Option A is not optimal because it does not address the memory issue of the web server. Option C is not optimal because it does not provide a persistent storage for the application data. Option D is not optimal because it does not provide a high availability and durability for the session data.

To run a containerized job hourly in ECS with minimal infrastructure management, the best choice is AWS Fargate. Fargate is a serverless compute engine for containers that runs ECS tasks without the developer needing to provision, patch, or scale EC2 instances.

With option C, the developer defines an ECS task definition using the Fargate launch type and schedules it (commonly via Amazon EventBridge Scheduler / EventBridge rule triggering RunTask). ECS handles the placement, networking, and execution of the task on managed infrastructure. This is the least operational overhead approach because there are no instances to manage and no capacity planning required, which fits an hourly job with small CPU/RAM needs.

Option A (capacity providers) is primarily for managing EC2 capacity for ECS and still requires underlying instances, which increases operational burden.

Option B runs the app on an EC2 instance, which requires instance lifecycle management, OS patching, scaling considerations, and failure handling.

Option D refers to managed node groups (an EKS concept) and does not apply cleanly to ECS. Even in a Kubernetes context, nodes still need management compared to Fargate.

Therefore, defining a Fargate task is the simplest and lowest-management solution.

The response from the API call indicates that the ProvisionedThroughputExceededException exception has occurred. This exception means that the rate of incoming requests exceeds the throughput limit for one or more shards in a stream. To mitigate this exception, the developer can use one or more of the following techniques:

Implement retries with exponential backoff. This will introduce randomness in the retry intervals and avoid overwhelming the shards with retries.

Reduce the frequency and/or size of the requests. This will reduce the load on the shards and avoid throttling errors.

Increase the number of shards in the stream. This will increase the throughput capacity of the stream and accommodate higher request rates.

Use a PutRecord API instead of PutRecords. This will reduce the number of records per request and avoid exceeding the payload limit.

Comprehensive and Detailed Step-by-Step Explanation:

Customer Managed Keys (CMK) for Granular Control (Option B):

Customer-managed KMS keys are required to meet the security policy requirement of team-specific control over KMS key rotation. Each team can manage the lifecycle of its own key.

The kms:Decrypt permission allows the Lambda function execution roles to decrypt the environment variables during runtime.

This solution adheres to the principle of least privilege and satisfies the need for team-specific key control.

Why Other Options Are Incorrect:

Option A: AWS-managed keys cannot provide team-specific control or support the custom rotation policy required by the teams.

Option C: Adding kms:CreateGrant and kms:Encrypt permissions to Lambda roles is unnecessary for this scenario. The key usage is limited to decryption at runtime.

Option D: AWS-managed keys still lack team-specific control, and adding kms:CreateGrant and kms:Encrypt is redundant.

To determine who deleted an Amazon RDS DB instance, the correct source of truth is AWS CloudTrail, which records API activity and includes the identity (IAM user, role, assumed role session) that made the call. Deleting an RDS instance is performed through the RDS API action DeleteDBInstance, and CloudTrail logs an event for this action that contains key fields such as userIdentity, eventTime, eventName, sourceIPAddress, and request parameters identifying the DB instance (for example, dBInstanceIdentifier).

Because the DB instance was deleted within the past 90 days, CloudTrail Event History is commonly sufficient (Event History typically retains 90 days of management events). If the company has a CloudTrail trail configured to deliver logs to S3/CloudWatch Logs, those logs can also be queried for the same event.

Option A directly matches this: retrieve CloudTrail events for DeleteDBInstance related to mysql-db and inspect the userIdentity section to identify the IAM principal that performed the deletion.

Option B is not reliable because RDS log groups (when enabled) capture database engine logs (slow query log, error log, general log) and do not record control-plane actions like who deleted the instance.

Option C is incorrect because X-Ray traces application-level request flows; it does not audit administrative actions like RDS deletion.

Option D is not applicable: Systems Manager inventory does not provide authoritative records of RDS deletions or the IAM principal responsible.

Therefore, CloudTrail lookup for DeleteDBInstance events is the correct solution.

Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with high availability and performance. DynamoDB can handle concurrent requests from multiple IoT devices without throttling or data loss. To prevent duplicate requests from causing inconsistencies or data loss, the Lambda function can use DynamoDB conditional writes to check if the unique identifier for each request already exists in the table before processing the request. If the identifier exists, the function can skip or abort the request; otherwise, it can process the request and store the identifier in the table. Reference: Using conditional writes

CloudWatch for Log Analysis: CloudWatch is the best fit here because logs are already centralized. Here's the process:

Metric Filter: Create a metric filter on the CloudWatch Logs log group. Design a pattern to specifically identify application error messages.

Custom Metric: This filter generates a new custom CloudWatch metric (e.g., ApplicationErrors). This metric tracks the error count.

CloudWatch Alarm: Create an alarm on the ApplicationErrors metric. Configure the alarm with your desired threshold and a 5-minute evaluation period.

SNS Action: Set the alarm to trigger an SNS notification when it enters the alarm state.

AWS Lambda supports versions and aliases to manage deployments safely. An alias is a named pointer to a specific function version (for example, prod → version 5). Importantly, Lambda aliases can be configured with routing configuration to split incoming invocation traffic between two versions. This enables canary or linear deployments where most traffic goes to the stable version and a small percentage goes to the new version for testing. If issues occur, the developer can quickly shift traffic back by updating the alias.

Option B is the native, simplest, and recommended solution for Lambda traffic shifting.

Option A is not correct because Route 53 weighted routing can split DNS queries between endpoints, but it does not natively target Lambda versions directly for most invocation patterns, and it’s not the standard mechanism for Lambda version traffic shifting.

Option C is unnecessary and adds operational overhead. ALB can invoke Lambda targets, but splitting between Lambda versions is more naturally handled by aliases without provisioning and managing an ALB.

Option D misunderstands layers: layers are for dependencies/shared code, not for deploying new versions or traffic splitting.

Therefore, using a Lambda alias with weighted routing between versions meets the requirement.

Amazon API Gateway provides tools for creating and documenting web APIs that route HTTP requests to Lambda functions2. You can secure access to your API with authentication and authorization controls. Your APIs can serve traffic over the internet or can be accessible only within your VPC2. You can override the cache method in the selected stage of API Gateway2. Therefore, option B is correct.

Lambda Destinations on Failure: Allow routing asynchronous function invocations to specified resources (like another Lambda function) upon failure.

Error Handling: The error-handling Lambda receives details about the failure, enabling logging and custom actions.

Direct Integration: This solution leverages native Lambda functionality for a simpler implementation.

AWS Lambda is a service that lets you run code without provisioning or managing servers. Lambda functions can be configured to access resources in a VPC, such as an Aurora database, by specifying one or more subnets and security groups in the VPC settings of the function. A security group acts as a virtual firewall that controls inbound and outbound traffic for the resources in a VPC. To allow a Lambda function to communicate with an Aurora database, both resources need to be associated with the same security group, and the security group rules need to allow TCP traffic on Port 3306, which is the default port for MySQL databases. Reference: [Configuring a Lambda function to access resources in a VPC]

For client-side encryption with AWS KMS, the documented pattern is envelope encryption: you use KMS to generate and protect a data encryption key (DEK), and you use that DEK locally to encrypt the actual data. This is the correct approach for large payloads (such as 10 MB documents), because the KMS Encrypt API is intended for encrypting small amounts of data (KMS is not designed to directly encrypt multi-megabyte application payloads).

With envelope encryption, the application calls GenerateDataKey on AWS KMS, specifying the KMS key (customer managed key) to use. KMS returns two versions of the DEK in the response:

a plaintext DEK (usable immediately by the application for local cryptographic operations), and

a ciphertext (encrypted) DEK that is encrypted under the specified KMS key.

The application then uses the plaintext DEK to encrypt the 10 MB document on the client side (for example, using an authenticated encryption mode such as AES-GCM via a standard crypto library). After encryption completes, the application must not persist the plaintext DEK; instead, it stores the encrypted document alongside the ciphertext DEK. Later, to decrypt, the application sends the ciphertext DEK to KMS using Decrypt to recover the plaintext DEK, and then decrypts the document locally.

Therefore, option D is correct because it explicitly uses GenerateDataKey and the plaintext DEK to encrypt the data, which is the required envelope-encryption flow. Options A, B, and C do not follow the correct KMS envelope-encryption process for large client-side payloads.

Default SSE in DynamoDB: DynamoDB tables are encrypted at rest by default using an AWS owned key (SSE-S3).

No Additional Action Needed: Creating a table without explicitly specifying a KMS key will use this default encryption.

The immutable deployment method is the best option for this scenario, because it meets the requirements of maintaining full capacity, avoiding service interruption, and minimizing the cost of additional resources.

The immutable deployment method creates a new set of instances in a separate Auto Scaling group and deploys the new version of the application to them. Then, it swaps the new instances with the old ones and terminates the old instances. This way, the application maintains full capacity during the deployment and avoids any downtime. The cost of additional resources is also minimized, because the new instances are only created for a short time and then replaced by the old ones.

The other deployment methods do not meet all the requirements:

The all at once method deploys the new version to all instances simultaneously, which causes a short period of downtime and reduced capacity.

The rolling with additional batch method deploys the new version in batches, but for the first batch it creates new instances instead of using the existing ones. This increases the cost of additional resources and reduces the capacity of the original environment.

The blue/green method creates a new environment with a new set of instances and deploys the new version to them. Then, it swaps the URLs between the old and new environments. This method maintains full capacity and avoids service interruption, but it also increases the cost of additional resources significantly, because it duplicates the entire environment.

The correct answer is C. The developer did not update the Docker image tag to a new version.

C. The developer did not update the Docker image tag to a new version. This is correct. When deploying an application to Amazon EKS, the developer needs to specify the Docker image tag that contains the application code and configuration. If the developer does not update the Docker image tag to a new version after making changes to the application, the EKS cluster will continue to use the old Docker image tag that references the original backend resources. To fix this issue, the developer should update the Docker image tag to a new version and redeploy the application to the EKS cluster.

A. The developer did not successfully create the new AWS account. This is incorrect. The creation of a new AWS account is not related to the application’s connection to the backend resources. The developer can use any AWS account to host the EKS cluster and the backend resources, as long as they have the proper permissions and configurations.

B. The developer added a new tag to the Docker image. This is incorrect. Adding a new tag to the Docker image is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

D. The developer pushed the changes to a new Docker image tag. This is incorrect. Pushing the changes to a new Docker image tag is not enough to deploy the changes to the application. The developer also needs to update the Docker image tag in the EKS cluster configuration, so that the EKS cluster can pull and run the new Docker image.

The requirement is to rotate database credentials at least weekly. The AWS service that is purpose-built for storing and automatically rotating secrets is AWS Secrets Manager. Secrets Manager supports rotation schedules (including daily, weekly, or custom intervals) and integrates with supported databases through rotation Lambda functions. With rotation enabled, Secrets Manager can periodically generate a new password, update the secret value, and apply the new credential to the database user, while allowing applications to retrieve the current credential securely at runtime.

Option C matches this best practice: create a database user, store the username/password in a Secrets Manager secret, and enable rotation (daily rotation more than satisfies a weekly requirement). Applications on EC2 can retrieve the secret at startup or on a refresh interval using the AWS SDK, and can cache it according to best practices to reduce API calls, while still supporting rotation.

Option A (Parameter Store secure string) encrypts parameters, but Parameter Store does not provide the same managed secret rotation workflow for database credentials; rotating the KMS key is not the same as rotating the database password. Option B is not applicable as stated for RDS for Microsoft SQL Server: IAM database authentication is supported for specific engines (commonly MySQL and PostgreSQL) and is not the standard mechanism for SQL Server password rotation. Option D is insecure and operationally fragile: embedding long-lived credentials in user data and environment variables makes rotation difficult and increases exposure risk.

Therefore, the most secure and compliant configuration is C: store DB credentials in AWS Secrets Manager and enable automated rotation on an interval that meets or exceeds the weekly rotation requirement.

Requirement Summary:

Lambda function accesses RDS for MySQL

Credentials are currently hardcoded in code (insecure)

Must enable automated credential rotation every 30 days

Option A: Use AWS Secrets Manager + automatic rotation

Best and secure option

Secrets Manager allows:

Secure storage of secrets

Integration with RDS for automatic rotation

Scheduled rotation every X days (e.g., 30 days)

Lambda can fetch credentials via SDK (GetSecretValue)

Option B: Use SSM Parameter Store + rotation

SSM does not support automatic rotation of secrets.

You'd need to build custom rotation logic = higher operational overhead.

Option C: Encrypted S3 + Object Lambda rotation

Not intended for credential storage.

S3 is not a secrets management system, and Object Lambda does not perform rotation.

Option D: SSM + EventBridge rotation

No native integration between Parameter Store and EventBridge for secret rotation.

You’d need to build custom Lambda functions = higher maintenance.

Secrets Manager rotation for RDS: https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html

Secure retrieval in Lambda: https://docs.aws.amazon.com/secretsmanager/latest/userguide/retrieving-secrets_lambda.html

Integration with RDS MySQL: https://docs.aws.amazon.com/secretsmanager/latest/userguide/integrating_rds_config.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/access-control-overview.html#access-control-resource-ownership

Requirement Summary:

Orders are being processed more than once

Need to prevent duplicate processing

Looking for least implementation effort

Key Concept:

Lambda + Event-driven patterns can occasionally result in duplicate invocations (at-least-once delivery model)

You need idempotency (i.e., prevent repeated processing of same event)

Evaluate Options:

A. Use DynamoDB for de-duplication

Simple and widely used approach

Store a unique orderId as the primary key

Before processing, check if order exists

If yes → skip

If no → process and store the ID

Minimal code changes required

B. ECS + Step Functions

Overkill for basic de-duplication

Adds significant complexity

C. Retry logic with fixed delay

Doesn't prevent duplication — makes it worse

Retrying might trigger the same message again

D. Athena to identify duplicates

Reactive solution, not preventative

Not suitable for real-time event de-duplication

Lambda idempotency: https://docs.aws.amazon.com/lambda/latest/dg/invocation-retries.html

Using DynamoDB for idempotent design: https://aws.amazon.com/blogs/compute/how-to-design-idempotent-APIs-on-aws/

The best solution for this requirement is option A. Creating an Amazon EventBridge rule that has a rate expression that will run the rule every 15 minutes and adding the Lambda function as the target of the EventBridge rule is the most efficient way to invoke the Lambda function periodically. This solution does not require any additional resources or development effort, and it leverages the built-in scheduling capabilities of EventBridge1.

Lambda Concurrency: The 'maximum concurrency' setting in event source mappings controls the maximum number of simultaneous invocations Lambda allows for that specific source.

Prioritizing Queues: Setting a lower maximum concurrency for the 'high priority queue' ensures it's processed first while allowing more concurrent invocations from the 'low priority queue'.

Batching: Batch size settings affect the number of messages Lambda retrieves from a queue per invocation, which is less relevant to the prioritization requirement.

Comprehensive and Detailed Step-by-Step Explanation:

Option D: Use Lambda with Container Images

AWS Lambda supports container images up to 10 GB in size, making it suitable for applications with large dependencies, such as a video codec library larger than 250 MB.

By creating separate container images for video compression and decompression, the application can efficiently isolate functionality while ensuring that each function includes the required dependencies.

The container images are stored in Amazon ECR and used to create the Lambda functions.

Why Other Options Are Incorrect:

Option A: A single Lambda function with all functionalities and dependencies in one zip file is not feasible due to the 250 MB deployment package size limit for zip files.

Option B: Including the library in two separate zip files still exceeds the size limit for Lambda zip deployment packages.

Option C: While using a Lambda layer can reduce redundancy, the combined size of the layer and the zip files would exceed the limit of 250 MB.

Comprehensive and Detailed Step-by-Step Explanation:

To ensure successful software deployments using AWS CodeDeploy, the application specification file (appspec.yml or appspec.json) must adhere to specific requirements:

File Format Requirement (Option B):

The appspec.yml file is a YAML-formatted file required for defining deployment actions and file locations. This is the modern and recommended format for application specification files.

It can also be a JSON-formatted file named appspec.json, but YAML is most commonly used and accepted.

File Placement Requirement (Option D):

The application specification file must reside in the root directory of the application's source code. This is necessary so that AWS CodeDeploy can detect and use the file during deployment.

Incorrect Options:

Option A: While a JSON-formatted file (appspec.json) is valid, this is not a mandatory requirement. YAML is also acceptable, and this option does not account for it.

Option C: The application specification file is not required to be stored in AWS CodeBuild; it must be included in the source code's directory structure.

Option E: The application specification file does not need to be stored in Amazon S3. S3 is commonly used for application artifacts, but the appspec.yml or appspec.json file must exist within the deployment package or source code root.

The core requirement is to perform a one-time deployment for testing without impacting the existing pre-production stack that other team members rely on. The safest way to avoid disruption is to deploy into an isolated AWS account (a dedicated development/testing account) instead of modifying the shared pre-production environment.

Option C achieves this with the least effort: use the AWS SAM CLI to package and deploy the same SAM application into a new AWS account designated for development. This creates an isolated copy of the stack (Lambda + SNS topics) where the developer can validate changes without affecting the shared pre-production deployment or the release pipeline.

Option A still deploys into pre-production, which directly violates the requirement to avoid impacting the existing application. A “debug parameter” does not prevent resource updates or side effects.

Option B is inconsistent: a CloudFormation change set is created against a specific account/stack. You cannot create a change set in one account and “execute it” in a different account as stated. Cross-account execution would require different credentials and a separate deployment process.

Option D introduces additional pipeline/stage complexity and is not necessary for a one-time test; it also implies changing the existing stack/pipeline, which can itself cause disruption.

Amazon CloudWatch is a service that monitors AWS resources and applications. The developer can publish custom metrics to CloudWatch that record the failures of the external payment processing API calls. The developer can configure a CloudWatch alarm to notify the existing SNS topic when the error rate exceeds 5% of the total number of transactions in an hour. This solution will meet the requirements in a near real-time and scalable way.

This solution meets the requirements most cost-effectively because it optimizes the photos on demand and caches them for future requests. Lambda@Edge allows the developer to run Lambda functions at AWS locations closer to viewers, which can reduce latency and improve photo quality. The developer can create a Lambda@Edge function that uses the GET parameters from each request to optimize the photos with the required dimensions and resolutions and returns them as a response. The function can also store a copy of the processed photos on Amazon S3 for subsequent requests, which can reduce processing time and costs. Using S3 Batch Operations to create new variants of the photos will incur additional storage costs and may not cover all possible dimensions and resolutions. Creating a dynamic CloudFront origin or a Lambda@Edge function to route requests to corresponding photo variants will require maintaining a mapping of device types and photo variants, which can be complex and error-prone.

AWS Secrets Manager supports multi-Region secret replication, which is designed specifically for redundancy, disaster recovery, and multi-Region applications. With this feature, the primary secret resides in one Region (here, us-west-1) and Secrets Manager automatically maintains a replica in another Region (us-east-1). This provides local read access and resilience if one Region is impaired.

Option A accurately describes the standard configuration: enable secret replication and add us-east-1 as the replica Region. Because encryption keys are Region-scoped, the replica secret in us-east-1 should be encrypted with a KMS key in us-east-1 (either the default Secrets Manager key for that Region or a customer managed key), satisfying encryption requirements and proper key locality.

Option B is incorrect because you don’t configure replication “from the destination.” Replication is configured on the primary secret, and the replica uses a KMS key in the replica Region, not in the source Region.

Option C is not how Secrets Manager replication works. Replication is not only during rotation; it maintains replicas continuously. The “replication rule during rotation” framing is not the standard mechanism.

Option D is inappropriate and insecure/operationally complex: exporting secrets to S3 for replication is not the recommended pattern and introduces unnecessary exposure.

Therefore, enable Secrets Manager multi-Region replication and encrypt replicas with a KMS key in the destination Region.

DynamoDB Accelerator (DAX) provides a managed caching layer specifically optimized for DynamoDB, reducing latency for repeated read requests.

Why Option A is Correct:

Purpose-Built: DAX is designed for DynamoDB, enabling sub-millisecond response times for frequently accessed items.

Operational Efficiency: No need for additional application-level caching logic.

Why Not Other Options:

Option B: Auto Scaling increases capacity but does not address repetitive reads.

Option C: API Gateway caching helps reduce request processing time but does not optimize DynamoDB reads.

Option D: ElastiCache is a general-purpose cache, adding unnecessary complexity for DynamoDB use cases.

DynamoDB Accelerator (DAX)

This solution allows the developer to overcome the limit for the combined maximum of characters for environment variables in AWS CodeBuild. AWS Systems Manager Parameter Store provides secure, hierarchical storage for configuration data management and secrets management. The developer can store large numbers of environment variables as parameters in Parameter Store and reference them in the buildspec file using parameter references. Adding export LC_ALL=“en_US.utf8” command to the pre_build section will not affect the environment variables limit. Using Amazon Cognito or an Amazon S3 bucket to store key-value pairs for environment variables will require additional configuration and integration.

This solution will meet the requirements by using AWS Secrets Manager, which is a service that helps protect secrets such as database credentials by encrypting them with AWS Key Management Service (AWS KMS) and enabling automatic rotation of secrets. The developer can use an AWS Secrets Manager resource in AWS CloudFormation template, which enables creating and managing secrets as part of a CloudFormation stack. The developer can use an AWS::SecretsManager::Secret resource type to generate and rotate the password for accessing RDS database during deployment. The developer can also specify a RotationSchedule property for the secret resource, which defines how often to rotate the secret and which Lambda function to use for rotation logic. Option A is not optimal because it will use an AWS Lambda function as a CloudFormation custom resource, which may introduce additional complexity and overhead for creating and managing a custom resource and implementing rotation logic. Option B is not optimal because it will use an AWS Systems Manager Parameter Store resource with the SecureString data type, which does not support automatic rotation of secrets. Option C is not optimal because it will use a cron daemon on the application’s host to generate and rotate the password, which may incur more costs and require more maintenance for running and securing a host.

To retrieve a secret’s value from AWS Secrets Manager, the calling principal must be authorized to call secretsmanager:GetSecretValue on the specified secret. This API returns the secret material (for example, SecretString or SecretBinary). Passing secret IDs through environment variables only tells the application which secret to request; it does not grant permission to access it.

Because the secret is encrypted with a customer managed AWS KMS key, the caller must also be allowed to use that key for decryption. Secrets Manager stores secret values encrypted at rest, and when a caller requests the secret value, KMS is used to decrypt the stored ciphertext under the configured CMK. Therefore, the principal needs kms:Decrypt permission on the CMK (and the CMK key policy must allow the principal, directly or via grants/conditions). Without kms:Decrypt, the GetSecretValue call will fail because Secrets Manager cannot return plaintext secret material.

secretsmanager:DescribeSecret (B) can be useful for reading metadata such as rotation configuration or tags, but it is not required to retrieve the secret value itself. secretsmanager:ListSecrets (C) is for discovery/enumeration and is not required when the application already knows the secret ID. kms:Encrypt (E) is not needed for reading a secret value; encryption permissions are relevant for write/update operations or client-side encryption flows, not for decrypting stored secrets.

Therefore, the required combination is A (secretsmanager:GetSecretValue) and D (kms:Decrypt) to successfully retrieve secret values encrypted with a customer managed KMS key.

The objects are “rarely accessed after 1 week” but must remain immediately available at all times. That means the storage class must support millisecond access without a restore process. S3 Standard-Infrequent Access (Standard-IA) is designed for exactly this: lower storage cost than S3 Standard, while still providing rapid access when needed.

With option B, an S3 Lifecycle rule transitions objects from S3 Standard to S3 Standard-IA after 7 days. This aligns perfectly with the usage pattern: frequent access initially, then infrequent access after a week, while keeping immediate availability.

Option C (S3 Glacier Flexible Retrieval) is not appropriate because Glacier classes are archival. Access typically requires a restore operation and retrieval time (minutes to hours), which violates “immediately available at all times.”

Option A expires objects, which deletes them after 7 days—this contradicts the requirement to keep objects available.

Option D is irrelevant because delete markers exist only when versioning is enabled. The bucket does not have versioning enabled, so this rule would not help.

Therefore, transitioning to S3 Standard-IA after 7 days is the correct cost-optimized solution while maintaining immediate availability.

Comprehensive and Detailed Explanation (250–300 words):

For in-place deployments, AWS CodeDeploy updates instances directly rather than using blue/green infrastructure. AWS documentation states that when automatic rollback is enabled and a deployment fails (for example, due to failed health checks or validation scripts), CodeDeploy initiates a rollback deployment.

In an in-place rollback, CodeDeploy redeploys the last known successful application revision to the affected instances. This rollback is treated as a new deployment, complete with its own deployment ID and lifecycle events. CodeDeploy does not use snapshots, Route 53 switching, or external promotion logic for in-place deployments.

Options A and B describe mechanisms that are used in other services or blue/green strategies, not in-place deployments. Option D is incorrect because CodePipeline orchestrates stages but does not perform rollback logic for CodeDeploy.

Therefore, redeploying the previous stable version as a new deployment is the correct behavior.

To securely grant temporary access to a specific S3 object, the best option is to use an Amazon S3 presigned URL. A presigned URL is created by an IAM principal (user or role) that already has permission to access the object. The URL includes a signature and an expiration time, allowing anyone who has the URL to perform the specified operation (typically GET to download, or PUT to upload) until the URL expires. This meets the requirement for temporary access without changing the bucket to be publicly accessible.

Presigned URLs are especially useful when the developer needs to share access with people who do not have AWS credentials or are not part of the same AWS account. The developer can set an expiration that matches the business need (minutes to hours, etc.). After expiration, the URL no longer works, which reduces security exposure compared with long-lived access keys or permanently permissive bucket policies.

Option C (bucket policy with time restriction) can enforce time-based access, but it is not a practical way to securely share with a “specific group of people” unless those people authenticate with identifiable principals (IAM roles/users, federated identities) and you scope the policy to those principals. Simply providing the bucket S3 URL would not grant access unless the recipients also have valid permissions. Option D (static website hosting) is generally for public web content and does not inherently provide secure, temporary, per-object access control. Option A is unrelated to access sharing; object retention and restore operations are for retention/compliance and archival retrieval, not temporary permission grants.

Therefore, generating and sharing a presigned URL (B) is the standard, secure mechanism for time-limited access to S3 objects.

The requirements call for (1) standardized templates to define serverless infrastructure, (2) the ability to test locally before deployment, and (3) integrating deployment and infrastructure changes into an existing AWS CodePipeline workflow. AWS Serverless Application Model (AWS SAM) is designed specifically for serverless applications and extends CloudFormation with serverless-specific resources (such as AWS::Serverless::Function, APIs, event sources). SAM also provides the SAM CLI, which includes local testing capabilities (for example, sam local invoke and sam local start-api) that emulate Lambda and API Gateway behavior for rapid iteration before deployment.

Option A fits all requirements: create an AWS SAM template as the infrastructure definition and add pipeline stages that execute the necessary SAM CLI commands (commonly sam build and sam deploy, and optionally test commands) as part of the CI/CD process. This makes deployments repeatable and automated, ensures infrastructure changes are deployed in lockstep with application changes, and keeps everything governed by the existing CodePipeline.

Option D uses SAM but relies on a standalone script outside the pipeline, which conflicts with the requirement to incorporate infrastructure changes into the existing pipeline. Option C (plain CloudFormation) can define infrastructure, but it does not inherently provide the same streamlined local testing workflow that SAM CLI provides for serverless event sources and APIs. Option B is unrelated: Step Functions is an orchestration service and its definition language is not a general infrastructure templating and local-testing solution for serverless deployments.

Therefore, A is the correct strategy: use AWS SAM templates and integrate SAM CLI build/deploy steps into CodePipeline to automate deployments and support local testing while managing infrastructure as code.

Amazon CloudWatch is the service that collects and stores logs from AWS Lambda functions. The developer can use CloudWatch Logs Insights to query and analyze the logs for errors and metrics. Option A is not correct because Amazon S3 is a storage service that does not store Lambda function logs. Option B is not correct because AWS CloudTrail is a service that records API calls and events for AWS services, not Lambda function logs. Option D is not correct because Amazon DynamoDB is a database service that does not store Lambda function logs.

The solution that will meet the requirements is to use CloudFormation to create an AWS Secrets Manager secret. Use a CloudFormation dynamic reference to retrieve the secret’s value for the OpenSearch Service domain’s MasterUserOptions. Create an IAM role that has the secretsmanager:GetSecretValue permission. Assign the role to the Lambda function. Store the secret’s name as the Lambda function’s environment variable. Resolve the secret’s value at runtime. This way, the developer can pass the credentials to the Lambda function in a secure way, as AWS Secrets Manager encrypts and manages the secrets. The developer can also use a dynamic reference to avoid exposing the secret’s value in plain text in the CloudFormation template. The other options either involve passing the credentials as plain text parameters, which is not secure, or encrypting them with AWS KMS, which is less convenient than using AWS Secrets Manager.

Step-by-Step Breakdown:

Requirement Summary:

AWS AppSync API needs to invoke Lambda functions

Some Lambda functions are long-running

AppSync should return immediately, minimizing operational overhead

**Option A: AppSync + Lambda as data source using Event Mode

Correct: AWS AppSync supports asynchronous (event) invocation of Lambda data sources using Event Mode.

Event Mode means:

AppSync invokes Lambda asynchronously

Immediately returns a response to the client (typically a predefined payload or null)

Ideal for fire-and-forget workloads or when the response is not immediately needed

Option B: Increase Lambda timeout

Incorrect: This keeps AppSync waiting.

Even with increased timeout, synchronous invocations would block AppSync responses.

Option C: SQS queue + polling Lambda

Possible but too complex for this use case.

Requires additional infrastructure: queue + mapping + custom logic.

Higher operational overhead compared to built-in AppSync Event Mode.

Option D: Enable caching in AppSync

Irrelevant: AppSync cache is for optimizing repeated read queries, not for async workflows.

Asynchronous Lambda with AppSync: https://docs.aws.amazon.com/appsync/latest/devguide/resolver-mapping-template-reference-lambda.html#async-lambda-invocation

Lambda as AppSync Data Source: https://docs.aws.amazon.com/appsync/latest/devguide/tutorial-lambda-resolvers.html

Event Mode Docs: https://docs.aws.amazon.com/appsync/latest/devguide/lambda-resolvers.html#event-invocation-mode

This solution allows the developer to create and delete branches in AWS CodeCommit by granting the codecommit:CreateBranch and codecommit:DeleteBranch permissions. These are the minimum permissions required for this task, following the principle of least privilege. Option B grants too many permissions, such as codecommit:Put*, which allows the developer to create, update, or delete any resource in CodeCommit. Option C grants too few permissions, such as codecommit:Update*, which does not allow the developer to create or delete branches. Option D grants all permissions, such as codecommit:*, which is not secure or recommended.

The most secure way to handle database credentials for a CodeBuild stage—especially with a company requirement for automatic rotation—is to store credentials in AWS Secrets Manager and reference the secret securely from the CodeBuild environment. Secrets Manager is purpose-built to store, retrieve, and rotate secrets such as database credentials, API keys, and tokens. It encrypts secrets at rest (using AWS KMS), supports fine-grained IAM access control, and integrates with services like CodeBuild through environment variables and runtime retrieval.

With CodeBuild, the developer can configure environment variables to reference a Secrets Manager secret (rather than embedding credentials in buildspec.yml). This ensures that the build process retrieves the latest rotated credentials at runtime, reducing exposure risk and eliminating manual credential updates.

Option B is weaker because Systems Manager Parameter Store (SecureString) is a secure storage mechanism, but automatic rotation is not a native Parameter Store feature in the same way it is in Secrets Manager for database credentials. Secrets Manager provides managed rotation workflows (often via Lambda rotation functions) and scheduling that directly matches the requirement.

Options A and D violate secure handling practices by hardcoding credentials or storing plaintext connection strings. Both approaches significantly increase risk of exposure through source control, build logs, or environment inspection. Additionally, bolting on rotation via Lambda or EventBridge does not address the primary weakness of hardcoded/plaintext secret distribution.

Therefore, AWS Secrets Manager with automatic rotation, referenced securely from CodeBuild environment variables, is the most secure solution.

Why Option B is Correct:The pilot light strategy keeps a minimal version of the environment in another Region and scales up during a disaster. It achieves an RTO and RPO of hours at a low cost and complexity.

Why Other Options are Incorrect:

Option A: Warm standby is more expensive as it keeps a scaled-down, fully functioning version running in another Region.

Option C: Backup and restore has a longer RTO and RPO than hours.

Option D: Multi-site active-active is costly and more complex than required.

AWS Documentation References:

Disaster Recovery Strategies on AWS

The key constraint is no changes to the Lambda code, while still fanning out notifications so that each department receives only its relevant file locations in its existing SQS queue. The cleanest “plumbing-only” pattern is S3 → SNS → SQS with SNS subscription filter policies.

Amazon S3 event notifications can publish events to an SNS topic. SNS then delivers messages to multiple subscribers, including SQS queues. By subscribing each department’s SQS queue to the SNS topic, the system can fan out the event to all queues. To ensure each department receives only its relevant events, the developer can configure SNS subscription filter policies (for example, based on object key prefixes like /deptA/, /deptB/). This routes messages without requiring any change to the Lambda function.

Option D is not ideal because S3 event notifications do not provide the same flexible filtering/routing to multiple SQS queues as SNS filter policies do (and configuring many direct notifications becomes harder to manage).

Options B and C require Lambda code changes, which violates the requirement.

Therefore, use S3 event notifications to SNS, subscribe each department SQS queue, and use filter policies for routing.

To measure how long an application spends calling downstream AWS services (for example, DynamoDB, S3, SNS, etc.) in a distributed system, the most effective AWS-native approach is AWS X-Ray with appropriate instrumentation. X-Ray provides distributed tracing by capturing end-to-end request paths and breaking each request into segments and subsegments with precise timing information. When the application is instrumented with the X-Ray SDK, client handlers/interceptors automatically create subsegments for calls to AWS services, recording latency, response status, and error/throttle indicators.

This directly satisfies the requirement: determine the length of time of calls to various downstream services. In the X-Ray trace map and trace details, the developer can see per-service timing, identify which dependency is slow, and distinguish application processing time from downstream call time. This is specifically designed for performance bottleneck analysis and root cause investigation across distributed components.

Option A (VPC Flow Logs) captures network flow metadata (source/destination, ports, bytes, accept/reject), not application-level request timing to AWS service APIs, and it won’t show the latency breakdown per downstream service call.

Option B can work only if the application is already logging detailed timing for each call, but this is manual, inconsistent across services, and does not give a unified distributed trace view. It is also higher effort than using standard tracing instrumentation.

Option C increases CloudWatch metric granularity for EC2 but does not show per-request downstream service call durations.

Therefore, implementing AWS X-Ray with client handlers is the correct solution.

Why Option A is Correct:DynamoDB supports incremental exports to Amazon S3 natively, making data analytics-ready with minimal operational overhead.

Why Other Options are Incorrect:

Option B: DynamoDB Streams require additional processing logic to write to S3, increasing complexity.

Option C & D: Using EMR for data movement adds unnecessary operational overhead compared to native exports.

AWS Documentation References:

DynamoDB Exports to S3

Why Option B is Correct:RDS Proxy manages database connections efficiently, reducing overhead on the RDS instance and mitigating "too many connections" errors.

Why Other Options are Incorrect:

Option A: DAX is for DynamoDB, not RDS.

Option C: Migration to PostgreSQL does not address the current issue.

Option D: ElastiCache is useful for caching but does not solve connection pool issues.

AWS Documentation References:

Amazon RDS Proxy

Using a dead-letter queue (DLQ) with Amazon SQS is the most operationally efficient solution for handling unprocessable messages.

Amazon SQS Dead-Letter Queue:

A DLQ is used to capture messages that fail processing after a specified number of attempts.

Allows the application to continue processing other messages without being blocked.

Messages in the DLQ can be analyzed later for debugging and resolution.

Why DLQ is the Best Option:

Operational Efficiency: Automatically defers messages with errors, ensuring the queue is not blocked.

Analysis Ready: Messages in the DLQ can be inspected to identify recurring issues.

Scalable: Works seamlessly with Lambda and SQS at scale.

Why Not Other Options:

Option A: Logs the messages but does not resolve the queue blockage issue.

Option C: FIFO queues and 0-second retention do not provide error handling or analysis capabilities.

Option D: Alerts administrators but does not handle or store the unprocessable messages.

Steps to Implement:

Create a new SQS queue to serve as the DLQ.

Attach the DLQ to the primary queue and configure the Maximum Receives setting.

Using Amazon SQS Dead-Letter Queues

Best Practices for Using Amazon SQS with AWS Lambda

Problem: CloudFormation updates reset Parameter Store parameters, disrupting application behavior.

Deletion Policy: CloudFormation has a deletion policy that controls resource behavior when a stack is deleted or updated. The 'Retain' policy instructs CloudFormation to preserve a resource's current state.

Least Development Effort: This solution involves a simple CloudFormation template modification, requiring minimal code changes.

The application relies on shared file storage that behaves like NFS and must be accessible concurrently by multiple containers across Kubernetes nodes. AWS documentation clearly identifies Amazon EFS as the AWS-native, fully managed, NFS-compatible file system designed for this purpose.

Amazon EFS provides elastic scaling, high availability across multiple Availability Zones, and native integration with container orchestration platforms. It is the most direct replacement for on-premises NFS, requiring minimal architectural change.

To run Kubernetes workloads on AWS with high availability, AWS recommends Amazon EKS, a managed Kubernetes service that automatically handles control plane availability and upgrades. EKS integrates natively with EFS using the Amazon EFS CSI driver, allowing pods across multiple nodes to mount the same file system concurrently.

Amazon EBS (Options A, C, and D) is block storage that can be attached to only one EC2 instance at a time (except in limited Multi-Attach scenarios) and is therefore unsuitable for shared NFS-style access across a Kubernetes cluster.

Uploading container images to Amazon ECR ensures secure, scalable storage for container images and seamless integration with EKS.

Therefore, migrating the NFS data to Amazon EFS and running the workloads on Amazon EKS is the fastest, most scalable, and AWS-recommended solution.

Maximum concurrency for SQS as an event source allows customers to control the maximum concurrent invokes by the SQS event source1. When multiple SQS event sources are configured to a function, customers can control the maximum concurrent invokes of individual SQS event source1.

In this scenario, the developer needs to resolve the issue of the third-party API frequently returning an HTTP 429 Too Many Requests error message, which prevents a significant number of messages from being processed successfully. To achieve this, the developer can follow these steps:

Find out the documented rate limits of the third-party API, which specify how many requests can be made in a given time period.

Configure maximum concurrency on the SQS event source based on the rate limits of the third-party API. This will limit the number of concurrent invokes by the SQS event source and prevent exceeding the rate limits of the third-party API.

Test and monitor the application performance and adjust the maximum concurrency value as needed.

By using this solution, the developer can reduce the frequency of HTTP 429 errors and improve the message processing success rate. The developer can also avoid throttling or blocking by the third-party API.

Amazon API Gateway mock integrations allow developers to return static or templated responses without invoking any backend services. AWS documentation specifically recommends mock integrations for early API development and testing, when backend implementations are unavailable or incomplete.

With mock integrations, API Gateway itself generates responses based on mapping templates. This enables frontend and integration testing teams to validate request formats, response schemas, authentication, and error handling without deploying compute resources or writing application logic.

Options B, C, and D all require additional infrastructure or application logic, increasing engineering effort. Mock integrations require no backend code, no servers, and minimal configuration.

Therefore, using API Gateway mock integrations is the fastest and lowest-effort solution.

To meet the requirement that developers cannot view credit card numbers while the fraud detection account can, the solution must (1) ensure the sensitive data is masked at ingestion/storage in CloudWatch Logs, and (2) allow only an authorized principal to view unmasked values.

First, the developers must attach the data protection policy to the CloudWatch log group that receives the API Gateway HTTP API access logs (and/or the Lambda log group if credit card numbers can appear there). A CloudWatch Logs data protection policy is applied at the log group level and instructs CloudWatch Logs to identify sensitive data using managed data identifiers (for example, CreditCardNumber) and then apply the configured de-identification action (here, masking). Without attaching the policy to the log group, the masking/redaction behavior will not be enforced for newly ingested log events, and developers could still see raw request parameters.

Second, to permit only the fraud detection account to reveal the masked values when necessary, the IAM role that the fraud detection account assumes must have the specific CloudWatch Logs permission to unmask protected data. Granting logs:Unmask to that role enables authorized access to view the sensitive values while keeping them masked for everyone else who lacks that permission (such as developer accounts assuming other roles).

Options A and E are not required for CloudWatch Logs data protection enforcement; the core controls are log-group policy attachment (to perform masking) and IAM authorization (to permit unmasking only for the fraud role). Therefore, the correct combination is C (apply the policy to the log group that captures the HTTP API logs) and B (grant logs:Unmask to the fraud detection role).

For this workload, the access pattern is centered on each user’s activity history over time (the company is planning a marketing campaign based on each user’s activity). In DynamoDB, the most effective way to store and query time-ordered events per entity is to use a composite primary key where the entity identifier is the partition key and a time attribute is the sort key. Therefore, using user ID as the partition key groups all events for a given user together, and using timestamp as the sort key allows efficient retrieval in chronological order and supports range queries (for example, “activities in the last 7 days” or “between two dates”).

This design also helps maximize provisioned throughput efficiency and reduce throttling risk because it distributes data across partitions based on the partition key values (user IDs). With many users, writes and reads naturally spread across multiple partition key values rather than concentrating on a small set. The sort key further enables multiple activity records per user without overwriting items and supports precise queries without scanning.

Option B (product ID as partition key) does not align with the stated marketing need (per-user activity). It would cluster all users’ actions for a popular product into the same partition key, increasing the chance of hot partitions and throttling. Option C also risks hot partitions for popular product IDs; auto scaling helps capacity management but does not fix poor key distribution or hot-key access patterns. Option D (monotonic counter as partition key) is particularly problematic: sequential keys can create uneven distribution and a throughput bottleneck pattern, and it does not support user-centric queries efficiently.

Thus, A best matches the data model (user events over time), supports efficient queries, and reduces throttling risk by distributing workload across user IDs.

This solution meets the requirements because it encrypts data at rest using AWS KMS keys and provides an audit trail of when and by whom they were used. Server-side encryption with AWS KMS managed keys (SSE-KMS) is a feature of Amazon S3 that encrypts data using keys that are managed by AWS KMS. When SSE-KMS is enabled for an S3 bucket or object, S3 requests AWS KMS to generate data keys and encrypts data using these keys. AWS KMS logs every use of its keys in AWS CloudTrail, which records all API calls to AWS KMS as events. These events include information such as who made the request, when it was made, and which key was used. The company policy can use CloudTrail logs to audit critical events related to their data encryption and access. Server-side encryption with Amazon S3 managed keys (SSE-S3) also encrypts data at rest using keys that are managed by S3, but does not provide an audit trail of key usage. Server-side encryption with customer-provided keys (SSE-C) and server-side encryption with self-managed keys also encrypt data at rest using keys that are provided or managed by customers, but do not provide an audit trail of key usage and require additional overhead for key management.

The requirements are: (1) store an API key that a Lambda function will use, (2) ensure the secret is encrypted at rest using AWS KMS, and (3) the company must control key rotation, which implies using a customer managed KMS key (CMK) rather than an AWS managed key.

Option C meets the requirements by storing the API key in AWS Systems Manager Parameter Store as a SecureString parameter encrypted with a customer managed KMS key. SecureString is designed for sensitive configuration data and integrates with KMS so the organization can choose the CMK, manage its lifecycle, and control rotation policies. The Lambda function can retrieve the parameter at runtime using the AWS SDK, and IAM policies can tightly control access to the parameter and to the KMS key.

Option E also meets the requirements by storing the API key in a Lambda environment variable encrypted with a customer managed KMS key. Lambda encrypts environment variables at rest and allows you to specify a customer managed KMS key for encryption. This gives the company control over key rotation and key policy, satisfying the “must control key rotation” requirement. The function reads the value from the environment at runtime without additional network calls.

Why the other options fail:

A uses an AWS managed KMS key, which does not satisfy the requirement for the company to control rotation (you cannot manage rotation of AWS managed keys in the same way).

B is a plain String parameter, which is not encrypted as a secret and does not meet the at-rest encryption requirement.

D uses an AWS managed KMS key, again failing the company-controlled rotation requirement.

Therefore, the two valid solutions are C (Parameter Store SecureString with a customer managed CMK) and E (Lambda environment variable encrypted with a customer managed CMK).

Comprehensive and Detailed Step-by-Step Explanation:

Option A: Use Amazon S3 for Shared Test Events:

Storing JSON test event files in an S3 bucket provides a centralized, cost-effective, and highly available solution.

Granular IAM policies can restrict access to specific developers or roles, ensuring security while maintaining consistency for shared test events.

This solution has minimal operational overhead and integrates easily with existing workflows.

Why Other Options Are Incorrect:

Option B: Using DynamoDB and a Lambda function introduces unnecessary complexity for a relatively simple requirement. S3 provides a simpler and more cost-efficient solution.

Option C: AWS Lambda test events are not inherently shareable across developers, making this option invalid.

Option D: Using a Git repository adds operational overhead and requires developers to clone/update repositories for access, which is more cumbersome compared to S3.

This solution allows the Lambda function to be invoked by the DynamoDB stream whenever updates are made to items in the DynamoDB table. Event source mapping is a feature of Lambda that enables a function to be triggered by an event source, such as a DynamoDB stream, an Amazon Kinesis stream, or an Amazon Simple Queue Service (SQS) queue. The developer can configure event source mapping for the Lambda function using the AWS Management Console, the AWS CLI, or the AWS SDKs. Changing the StreamViewType parameter value to NEW_AND_OLD_IMAGES for the DynamoDB table will not affect the invocation of the Lambda function, but only change the information that is written to the stream record. Mapping an Amazon Simple Notification Service (Amazon SNS) topic to the DynamoDB stream will not invoke the Lambda function directly, but require an additional subscription from the Lambda function to the SNS topic. Increasing the maximum runtime (timeout) setting of the Lambda function will not affect the invocation of the Lambda function, but only change how long the function can run before it is terminated.

The solution that will meet the requirements is to use an AWS Step Functions state machine to monitor API failures. Use the Wait state to delay calling the Lambda function. This way, the developer can refactor the serverless application to accommodate the change in a way that is automated and scalable. The developer can use Step Functions to orchestrate the Lambda function and handle any errors or retries. The developer can also use the Wait state to pause the execution for a specified duration or until a specified timestamp, which can help avoid exceeding the API limits. The other options either involve using additional services that are not necessary or appropriate for this scenario, or do not address the issue of API failures.

The requirement is specifically encryption at rest for messages stored in Amazon SQS. The AWS-native way to accomplish this is to enable server-side encryption (SSE) on the SQS queue. When SSE is enabled, SQS encrypts messages as they are stored and decrypts them when they are retrieved, using keys from AWS Key Management Service (AWS KMS) (either an AWS managed key for SQS or a customer managed KMS key). This provides transparent encryption at rest without requiring application-level cryptography changes.

Option C correctly states to enable SSE on the queue. Once enabled, any message content placed in the message body will be protected at rest in SQS. (Message attributes are also stored by SQS; however, the key requirement is to ensure the messages are encrypted at rest, and enabling SSE at the queue level is the control that enforces this.)

Option A (aws:SecureTransport) enforces encryption in transit by requiring HTTPS/TLS, not encryption at rest. It is a good security control, but it does not satisfy the at-rest requirement by itself. Option B is incorrect because SSE is not something the sender “sets within the message”; SSE is configured on the queue, not per-message by the microservices. Option D suggests putting sensitive data in attributes, which does not add security; it can actually increase exposure because attributes are often used for routing/filters and may be logged or surfaced in monitoring. The right pattern is to keep sensitive data where intended and rely on queue-level SSE to encrypt stored messages.

Therefore, enabling SQS SSE on the queue (C) meets the requirement to ensure messages are encrypted at rest while stored in SQS.

Requirement Summary:

Prevent unauthorized code changes in AWS Lambda

Ensure only trusted code is deployed

AWS Lambda supports Code Signing:

You can configure code signing in Lambda using AWS Signer

Packages must be digitally signed and verified against the signing profile

Rejects unauthorized/modified packages automatically

Evaluate Options:

A. Trusted code option in CodeDeploy

No such feature exists for Lambda

CodeDeploy is more for EC2/On-Prem/Containers, not Lambda code signing

B. Define code signing config + use AWS Signer

This is exactly how AWS enforces trusted code deployment

Attach a code signing configuration to the Lambda function

Use AWS Signer to digitally sign deployment packages

C. Link to KMS to sign code

KMS is not used to sign Lambda packages

KMS is for data encryption, not application code integrity

D. Set KmsKeyArn

This configures data encryption, not code signing

Lambda code signing: https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html

AWS Signer overview: https://docs.aws.amazon.com/signer/latest/developerguide/what-is-signer.html

The solution that will meet the requirements is to use the AWS SDK ssm PutParameter operation in the Lambda function from the existing custom resource to store the credentials as a parameter. Set the parameter value to reference the new credentials. Set the parameter type to SecureString. This way, the developer can store the API credentials with minimal operational overhead, as AWS Systems Manager Parameter Store provides secure and scalable storage for configuration data. The SecureString parameter type encrypts the parameter value with AWS Key Management Service (AWS KMS). The other options either involve adding additional resources to the CloudFormation template, which increases complexity and cost, or do not encrypt the parameter value, which reduces security.

Comprehensive and Detailed 250 to 300 words of Explanation From AWS Developer Documents:

The key requirement in this scenario is that the data must be encrypted before it is uploaded to Amazon S3, and the data is generated outside of AWS. This explicitly points to a client-side encryption requirement.

AWS documentation distinguishes clearly between server-side encryption and client-side encryption. Server-side encryption options for Amazon S3—including enabling default bucket encryption or specifying the x-amz-server-side-encryption request header—encrypt the data after it is received by Amazon S3. Therefore, options B and D do not satisfy the requirement because the plaintext data is transmitted to AWS before encryption occurs.

Option A, using the AWS KMS encrypt command, is not suitable for large binary objects. AWS KMS is designed to encrypt small pieces of data (up to 4 KB). AWS documentation explicitly states that for large payloads, customers should use envelope encryption instead of directly encrypting data with AWS KMS.

The AWS Encryption SDK is specifically designed for client-side encryption of large data objects. It implements envelope encryption, where a data encryption key (DEK) is generated locally to encrypt the data, and the DEK is then encrypted with an AWS KMS key. This approach allows efficient encryption of large binary files while maintaining strong key management and security controls.

AWS documentation recommends the AWS Encryption SDK for applications that need to encrypt data before sending it to AWS services, including Amazon S3. It supports multiple programming languages, handles key management automatically, and ensures data confidentiality even before transmission.

Therefore, using the AWS Encryption SDK for client-side encryption is the only solution that fully satisfies all requirements.

The requirement is encryption in transit for sensitive data sent in API calls between two Apache-based EC2 fleets in peered VPCs. The most operationally efficient approach is to restrict network access and rely on standard TLS at the application layer. Among the provided options, the only operationally simple and directly relevant network control is security group referencing across VPC peering within the same account and Region.

By creating security groups that allow inbound traffic only from the other fleet’s security group, the developer ensures that only the intended instances can communicate over the required ports (for example, 443 for HTTPS). This is operationally efficient because it avoids managing IP allowlists that change with Auto Scaling and keeps the trust boundary tied to instance membership rather than addresses. It also supports least privilege by limiting which sources can reach the service.

Why the other options are not the best fit:

B (Site-to-Site VPN) is unnecessary for VPC peering in the same account/Region and adds significant operational overhead. It also does not replace the need for TLS at the application layer to guarantee encryption to the endpoint.

C (EBS encryption) addresses encryption at rest on volumes, not encryption in transit between fleets.

D (Nitro Enclaves) is heavy and unrelated to the core requirement; enclaves protect data-in-use and do not replace standard TLS for network encryption.

Important nuance: Security groups alone do not encrypt traffic; encryption is achieved by using HTTPS/TLS between the Apache services. However, option A is the most operationally efficient control offered here to implement secure connectivity between the fleets (paired with using HTTPS on Apache, which is the standard for encrypting API calls).

Therefore, A best meets the requirement with the least operational complexity.

Amazon ElastiCache is a service that offers fully managed in-memory data stores that are compatible with Redis or Memcached. The developer can create an ElastiCache for Redis instance and enable encryption of data in transit and at rest. This will ensure that the PHI is encrypted at all times. The developer can store frequently accessed data in the cache and use Redis features such as sorting and ranking to enhance the performance of the application.

Why Option C is Correct:Ensuring that pipeline artifacts are encrypted with a customer-managed AWS KMS key involves configuring the S3 bucket policy to require encryption. This policy ensures all objects uploaded to the bucket are encrypted with the specified KMS key.

Why Other Options are Incorrect:

Option A: A gateway endpoint improves S3 access efficiency but does not enforce encryption.

Option B: Encrypting CloudWatch Logs is unrelated to securing pipeline artifacts.

Option D: Configuring SNS for encryption does not affect the artifacts stored in the S3 bucket.

AWS Documentation References:

Using Server-Side Encryption with S3 Bucket Policies

AWS Lambda SnapStart is designed specifically to reduce cold start latency for Java and .NET runtimes. SnapStart works by initializing the function once, taking a snapshot of the execution environment, and then reusing that snapshot for future cold starts. This dramatically reduces startup time, often from several seconds to milliseconds.

SnapStart requires the function to be published as a version, and an alias must reference that version. AWS documentation clearly states that SnapStart is the recommended solution for latency-sensitive Lambda workloads with infrequent invocations.

Reserved concurrency (Option B) limits scaling but does not eliminate cold starts. Lambda layers (Option C) improve code organization but have minimal impact on cold start latency. Adding the function to a VPC (Option D) typically increases cold start time due to network interface initialization.

Therefore, enabling SnapStart on a published .NET Lambda function is the correct and AWS-endorsed solution.

Because the developer cannot change the legacy application code, the solution must apply controls at the logging layer. CloudWatch Logs provides data protection policies that can automatically detect sensitive data using managed data identifiers (including credit card data) and apply masking (de-identification) as log events are ingested. This ensures that sensitive values such as card numbers and verification codes are not displayed in plaintext to general viewers of the log group.

With option A, enabling a data protection policy on the log group and configuring it to mask the relevant credit card data ensures that when support staff query or view logs, the sensitive fields appear redacted. CloudWatch Logs also supports controlled access to view the original unmasked values through an explicit permission: users who have the appropriate IAM authorization (such as logs:Unmask) can retrieve or view the sensitive data, while everyone else sees only the masked version. Granting logs:Unmask to application administrators satisfies the requirement that administrators can access the sensitive data, while withholding it from support staff enforces the restriction.

Option B (KMS encryption) provides encryption at rest for the log group but does not prevent authorized readers from seeing sensitive data once decrypted. Support staff who can read logs would still see plaintext card data. Option C is not a standard or operationally efficient pattern for CloudWatch Logs redaction; Macie is primarily for discovering sensitive data in S3 and does not serve as the native masking mechanism for CloudWatch Logs ingestion. Option D is not applicable: AWS WAF inspects HTTP requests/responses at the edge or load balancer/API layer, not application log streams already being emitted to CloudWatch.

Therefore, A is the correct solution: use CloudWatch Logs data protection to mask sensitive data by default and grant logs:Unmask only to administrators.

Why Option B is Correct:The RetainExceptOnCreate deletion policy ensures that the IAM user is retained after successful stack creation but is deleted if the stack creation fails or rolls back. This meets both requirements.

Why Other Options are Incorrect:

Option A: The Retain policy retains the resource regardless of stack status and does not delete the IAM user upon rollback.

Option C: Updating the service role for termination protection does not address the specific deletion behavior for the IAM user.

Option D: Stack policy controls updates, not resource deletion behavior during rollbacks.

AWS Documentation References:

CloudFormation DeletionPolicy Attribute

Step 1: Understanding the Requirements

Gradual Traffic Shift: Test the new version by routing only 10% of production traffic to it.

Lambda Deployment: Use versioning and aliases to manage Lambda function updates.

Step 2: Solution Analysis

Option A:

Publishing a new version creates an immutable version of the Lambda function with the updated code.

This is a prerequisite for deploying changes using weighted aliases.

Correct option.

Option B:

API Gateway stages are not used for weighted routing; they represent environments like "dev" or "prod."

Weighted routing is implemented using Lambda aliases, not API Gateway stages.

Not suitable.

Option C:

Lambda aliases allow traffic to be split between versions using weighted routing.

Assign a 90% weight to the old version and 10% to the new version to implement the gradual rollout.

Correct option.

Option D:

Network Load Balancers are not suitable for managing Lambda function traffic directly.

Not applicable.

Option E:

Route 53 routing policies apply at the DNS level and are not designed for Lambda version management.

Not suitable.

Step 3: Implementation Steps

Publish a New Version:

Publish the updated Lambda function code as a new version.

Create an Alias and Configure Weighted Routing:

Create an alias (e.g., prod) and associate it with both the old and new versions.

Set weights for traffic distribution:

aws lambda update-alias --function-name my-function \

--name prod --routing-config '{"AdditionalVersionWeights": {"2": 0.1}}'

AWS Developer References:

Lambda Function Aliases

Weighted Traffic Routing with Lambda

Configuring the Lambda function to use ephemeral storage and processing data in the /tmp directory improves performance by leveraging local storage during execution.

Why Option C is Correct:

Ephemeral Storage: Lambda provides temporary storage (up to 10 GB) in the /tmp directory for each invocation, which is faster than pulling data directly from S3 multiple times.

Performance Boost: Data can be downloaded to /tmp, processed locally, and uploaded to the destination S3 bucket, minimizing S3 network calls.

Low Overhead: This approach requires only minimal changes to the function's configuration.

Why Not Other Options:

Option A: Using Amazon EFS adds complexity and is unnecessary for this use case.

Option B: Scheduling the function does not address the root cause of slow performance.

Option D: Lambda layers improve deployment efficiency, not runtime performance for this scenario.

Using Ephemeral Storage in AWS Lambda

Best Practices for S3 and Lambda Performance

Step-by-Step Breakdown:

Requirement Summary:

In-transit encryption (when data is moving between client and S3)

Encryption at rest using AWS KMS keys (which are rotatable on-demand)

Option A: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using permissions boundary

Incorrect: Permissions boundaries are for IAM policy control, not S3 bucket policies.

Also, encryption enforcement in-transit is not achieved through permissions boundaries.

Option B: Configure an S3 bucket policy to enable client-side encryption

Incorrect: AWS does not enforce client-side encryption via S3 bucket policies.

Client-side encryption must be handled entirely by the application.

Also, KMS is for server-side encryption, not client-side.

Option C: Configure the application to encrypt the objects by using an AWS KMS customer managed key before uploading the objects

Correct: This fulfills the requirement of encrypting objects at rest using a KMS CMK, which can be rotated.

You can specify the KMS key using the SSE-KMS option when putting an object.

Option D: Write an S3 bucket policy to allow only encrypted connections over HTTPS by using the aws:SecureTransport condition

Correct: This enforces in-transit encryption, which ensures that only HTTPS connections are allowed.

This is the AWS-recommended way to enforce encryption in transit via bucket policy.

Option E: Configure S3 Block Public Access settings for the S3 bucket to allow only encrypted connections over HTTPS

Incorrect: Block Public Access is used to prevent public exposure of the bucket.

It has nothing to do with encryption enforcement, whether in transit or at rest.

Using SSE-KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html

aws:SecureTransport condition in bucket policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/amazon-s3-policy-keys.html

Server-side encryption with AWS KMS: https://docs.aws.amazon.com/AmazonS3/latest/userguide/serv-side-encryption.html

This solution will meet the requirements by using AWS Secrets Manager, which is a service that helps protect secrets such as database credentials by encrypting them with AWS Key Management Service (AWS KMS) and enabling automatic rotation of secrets. The developer can create an AWS Lambda function by using the SecretsManagerRotationTemplate template in the AWS Secrets Manager console, which provides a sample code for rotating secrets for RDS DB instances, Amazon DocumentDB clusters, and Amazon Aurora DB instances. The developer can also create secrets for the database credentials in Secrets Manager, which encrypts them at rest and provides secure access to them. The developer can set up secrets rotation on a schedule, which changes the database credentials periodically according to a specified interval or event. Option A is not optimal because it will set up IAM database authentication for token-based access, which may not be compatible with all database engines and may require additional configuration and management of IAM roles or users. Option B is not optimal because it will create parameters for the database credentials in AWS Systems Manager Parameter Store, which does not support automatic rotation of secrets. Option C is not optimal because it will store the database access credentials as an encrypted Amazon S3 object in an S3 bucket, which may introduce additional costs and complexity for accessing and securing the data.

Because the Lambda function is successfully triggered by the S3 event notification, the invocation path (S3 → Lambda) is working correctly. The failure occurs specifically when the function tries to write to DynamoDB, which strongly indicates an authorization problem rather than an invocation, scaling, or infrastructure issue.

In AWS, a Lambda function interacts with other services by using its execution role (an IAM role). AWS documentation explains that a Lambda function must have explicit IAM permissions to call downstream services such as DynamoDB. To write items, the role typically needs actions like dynamodb:PutItem (and sometimes dynamodb:UpdateItem, dynamodb:BatchWriteItem, depending on code behavior) on the target table resource ARN. If these permissions are missing or scoped incorrectly, DynamoDB returns an AccessDeniedException (or similar) and the function fails at the write step.

Option A is unlikely because exceeding concurrency would typically prevent invocation or cause throttling at the Lambda service level, not selectively fail only at DynamoDB write time after the function begins executing.

Option B is incorrect: DynamoDB does not require a GSI to support writes. GSIs are for alternate query access patterns, not mandatory for write operations.

Option D is incorrect because DynamoDB is a regional service, not tied to a single Availability Zone, and Lambda does not need to be “in the same AZ” to access it.

Therefore, the most likely cause is that the Lambda execution role lacks the necessary IAM permissions to perform DynamoDB write operations.

In AWS X-Ray, filter expressions can filter trace data based on indexed fields that X-Ray can query efficiently. Custom data can be added to segments in two main ways: annotations and metadata. The critical difference is that annotations are indexed and can be used for filtering, while metadata is not indexed and is intended for additional diagnostic context that you do not typically query on.

Therefore, if the developer wants user-specified custom attributes to be usable in X-Ray filter expressions, the developer should record those attributes as annotations in the segment document. Once recorded as annotations, the developer can write filter expressions that match annotation keys/values and return only the relevant traces.

Option B is incorrect because metadata is not indexed and cannot be used in filter expressions for trace search the same way annotations can.

Option C is incorrect because segment documents have a defined schema; adding arbitrary “new segment fields” is not the intended method for searchable custom attributes.

Option D is unrelated: sampling rules control which requests get traced in the first place. They are not used to filter returned results by custom attributes after traces are recorded.

“Encryption in transit” means the connection between the client (EC2 instances) and Amazon S3 must use TLS/HTTPS, not plain HTTP. The most direct way to enforce this at the bucket level is an S3 bucket policy that denies any request that is not using secure transport. AWS provides the global condition key aws:SecureTransport, which evaluates to true when a request is made over HTTPS and false when made over HTTP.

By adding an explicit Deny statement conditioned on aws:SecureTransport = false, the bucket rejects any insecure requests regardless of who makes them. This ensures that even if an application is misconfigured (or a user tries to access the bucket over HTTP), S3 will refuse the request. This is a strong enforcement mechanism because explicit denies in IAM policies override allows.

Option C (SSE-KMS) provides encryption at rest, not in transit. It protects objects while stored in S3 but does not force clients to use TLS for transport. Option B (VPC endpoint) can keep traffic on the AWS network path and can be combined with endpoint policies, but it does not inherently guarantee TLS usage unless you also enforce HTTPS. Option A (installing certificates) is not the mechanism for controlling whether the application uses HTTPS to S3; the S3 client uses AWS-managed TLS endpoints, and enforcement should happen through policy.

Therefore, the correct solution is D: create an S3 bucket policy that denies requests when aws:SecureTransport is false, ensuring all access uses encrypted transport (HTTPS/TLS).

The requirement is for a single secret value to be available in multiple AWS Regions while remaining consistent across those Regions, with the least operational overhead. AWS Secrets Manager provides a managed capability for this use case: secret replication (also referred to as multi-Region secrets). When replication is enabled from a primary Region, Secrets Manager automatically creates and maintains synchronized replicas of the secret in the selected secondary Regions. Updates to the primary secret are propagated to the replicas so the secret value remains consistent across Regions.

This approach (option C) minimizes operational effort because AWS manages the replication workflow, consistency, and the lifecycle of replicas. The application in each Region can retrieve the secret from the local Region endpoint of Secrets Manager by referencing the Region-appropriate secret ARN, improving latency and reducing cross-Region dependency. It also improves resilience: if a Region has issues, workloads in another Region can still read the replicated secret locally.

Option A recreates managed replication with custom glue (Lambda + EventBridge), which adds code, scheduling, error handling, permissions, retries, and monitoring—higher operational overhead and more failure modes. Option B introduces a cross-Region runtime dependency on the primary Region and increases latency; local caching also risks stale secret values during rotations/updates unless carefully managed, and it reduces resilience if the primary Region is impaired. Option D (independent secrets per Region) increases operational overhead and directly risks inconsistency, because keeping values synchronized becomes a manual or custom automated process.

Therefore, C is the best solution: enable Secrets Manager replication in the primary Region and have applications in each Region read from their local replicated secret ARN to achieve consistent, multi-Region availability with minimal operational work.

The described behavior is a classic cache stampede (also called the “thundering herd” problem): a large set of hot keys share the same expiration time, so they all expire simultaneously. When that happens, many requests miss the cache at the same moment and fall back to the backend database, creating a sudden spike in database load and causing increased latency and poor user experience.

The most effective fix that preserves freshness is to introduce jitter—a small, randomized variance—into the TTL values so cached objects do not all expire at the same time. With TTL jitter, each item still has a bounded lifetime (so freshness is maintained), but expirations become spread out over time. That smooths cache-miss rates and prevents synchronized regeneration, reducing bursts of database traffic. This is a common best practice in high-load caching systems precisely to avoid mass expiration events.

Option A (increase TTL) might reduce how frequently a stampede occurs, but it does not solve the root cause: keys can still align and expire together, and a longer TTL can also reduce freshness if product data changes. Option B (more replicas, disable cluster mode) changes topology and read scaling, but it does not prevent synchronized expiry or the resulting backend spike; disabling cluster mode could also reduce scale for large keyspaces. Option D (more shards) can improve cache throughput capacity, but it does not address the core issue that backend load spikes occur when items expire simultaneously; lowering replica count could even reduce read availability.

Therefore, C is the correct solution: keep TTL-based freshness, but add a randomized component (for example, TTL = baseTTL + random(0..jitter)) so expirations are distributed and the backend database is protected from sudden bursts.

Amazon S3 is a service that provides highly scalable, durable, and secure object storage. The developer can create an S3 bucket to host the repository and migrate the existing .xml files to the S3 bucket. The developer can update the application code to use the AWS SDK to read and write configuration files from S3. This solution will meet the requirement of high availability for the repository in a cost-effective way.

The combination of steps that the developer should take to fix the errors is to deploy AWS X-Ray as a sidecar container to the microservices and instrument the ECS task to send the stdout and stderr output to Amazon CloudWatch Logs. This way, the developer can use AWS X-Ray to analyze and debug the performance of the microservices and identify any issues or bottlenecks. The developer can also use CloudWatch Logs to monitor and troubleshoot the logs from the ECS task and detect any errors or exceptions. The other options either involve using AWS X-Ray as a daemon set, which is not supported by Fargate, or using the PutTraceSegments API call, which is not necessary when using a sidecar container.

Amazon SNS supports server-side encryption (SSE) using AWS KMS to protect message content at rest. Enabling SSE on the SNS topic ensures that all messages published to the topic are encrypted using a customer-managed or AWS-managed KMS key. AWS documentation explicitly states that SSE is the mechanism for encrypting SNS messages stored within the service.

For encryption in transit, AWS services rely on HTTPS/TLS. To enforce this at the IAM policy level, AWS recommends using the global condition key aws:SecureTransport. By adding an explicit Deny statement when "aws:SecureTransport": "false", the policy ensures that any request not using HTTPS is rejected. This is a common AWS security best practice and is documented across multiple AWS services.

Option E correctly enforces encryption in transit. Option B is incorrect because denying when SecureTransport is true would block valid HTTPS requests. Option D is invalid because sns:Protocol applies to subscription protocols (such as email or HTTP endpoints), not to Lambda publishing actions. Option C (VPC endpoint) is not required because Lambda-to-SNS traffic is already encrypted over TLS by default.

Therefore, enabling SNS server-side encryption and enforcing HTTPS via an IAM deny condition fully satisfies the encryption-at-rest and encryption-in-transit requirements.

The hourly scheduled batch model is causing timeouts because the function must process an ever-growing backlog in a single invocation, which can exceed AWS Lambda’s maximum 15-minute runtime. A best-practice serverless refactor is to switch from “periodic polling” to an event-driven approach where work is split into smaller, independent units that can scale horizontally.

Configuring Amazon S3 Event Notifications to invoke Lambda on each object upload turns each file into its own discrete event. Instead of one long-running invocation that scans and processes many files, Lambda executes quickly per upload and automatically scales with incoming volume (subject to concurrency and downstream limits). This directly reduces processing time per run and eliminates the backlog buildup that leads to timeouts.

The remaining requirement is preventing duplicate processing. S3 event delivery is designed to be highly reliable, and applications should be built with idempotency in mind. Recording processed identifiers (such as a transaction ID, object key + version ID, or an application-level unique ID) in Amazon DynamoDB allows the function to check whether an item has already been processed before performing writes or downstream actions. Using DynamoDB with a conditional write (for example, “insert only if not exists”) is a common pattern to ensure exactly-once effect even when events are delivered more than once.

The other options do not meet the requirements as well: running more frequently (C) or continuously polling (D) still wastes time scanning and can still create duplicate work; moving to a single EC2 instance (B) reduces elasticity and increases operational overhead, and it doesn’t inherently solve deduplication or scaling.

Therefore, A provides scalable, event-driven processing and a straightforward deduplication mechanism.

AWS Secrets Manager is a service that allows you to store and manage secrets, such as database credentials, API keys, and passwords, in a secure and centralized way. It also provides features such as automatic secret rotation, auditing, and monitoring1. By using AWS Secrets Manager, you can avoid hardcoding credentials in your code, which is a bad security practice and makes it difficult to update them. You can also replicate your secrets to another Region, which is useful for disaster recovery purposes2. To access your secrets from your application, you can use the ARN of the secret, which is a unique identifier that includes the Region name. This way, your application can use the appropriate secret based on the Region where it is deployed3.

AWS Secrets Manager

Replicating and sharing secrets

Using your own encryption keys