DVA-C02 AWS Certified Developer - Associate Questions and Answers

This solution will meet the requirements by using AWS Secrets Manager and AWS Systems Manager Parameter Store to securely store the parameter values outside the code in an encrypted format. AWS Secrets Manager 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 RDS database secret in AWS Secrets Manager and set the user name, password, database, host, and port for accessing the RDS database. The developer can also turn on secret rotation, which will change the database credentials periodically according to a specified schedule or event. AWS Systems Manager Parameter Store is a service that provides secure and scalable storage for configuration data and secrets. The developer can create Secure String parameters in AWS Systems Manager Parameter Store for the DynamoDB table, S3 bucket, and SNS topic, which will encrypt them with AWS KMS. The developer can also reuse the parameter values from other applications and update them without modifying code. Option A is not optimal because it will create encrypted Lambda environment variables for the DynamoDB table, S3 bucket, and SNS topic, which may not be reusable or updatable without modifying code. Option C is not optimal because it will create RDS database parameters in AWS Systems Manager Parameter Store, which does not support automatic rotation of secrets. Option D is not optimal because it will store the DynamoDB table, S3 bucket, and SNS topic in Amazon S3, which may introduce additional costs and complexity for accessing configuration data.

References: AWS Secrets Manager, [AWS Systems Manager Parameter Store]

The GenerateDataKey API returns a data key that is encrypted under a symmetric encryption KMS key that you specify, and a plaintext copy of the same data key1. The data key is a random byte string that can be used with any standard encryption algorithm, such as AES or SM42. The plaintext data key can be used to encrypt or decrypt data outside of AWS KMS, while the encrypted data key can be stored with the encrypted data and later decrypted by AWS KMS1.

In this scenario, the developer needs to use the GenerateDataKey API to encrypt the PDF file so that it can be decrypted later. The developer also needs to use an AWS KMS symmetric customer managed key for encryption. To achieve this, the developer can follow these steps:

Call the GenerateDataKey API with the symmetric customer managed key ID and the desired length or specification of the data key. The API will return an encrypted data key and a plaintext data key.

Write the encrypted data key to disk for later use. This will allow the developer to decrypt the data key and the PDF file later by using AWS KMS.

Use the plaintext data key and a symmetric encryption algorithm to encrypt the PDF file. The developer can use any standard encryption library or tool to perform this operation, such as OpenSSL or AWS Encryption SDK.

Discard the plaintext data key from memory as soon as possible after using it. This will prevent unauthorized access or leakage of the data key.

This solution will meet the requirements by using AWS Config to monitor and evaluate whether Secrets Manager secrets are unused or have been deleted, based on specified time periods. The secrets manager-secret-unused managed rule is a predefined rule that checks whether Secrets Manager secrets have been rotated within a specified number of days or have been deleted within a specified number of days after last accessed date. The Amazon EventBridge rule will trigger a notification when the AWS Config managed rule is met, alerting the developer about unused secrets that can be removed without causing application downtime. Option A is not optimal because it will use AWS CloudTrail log file delivery to an Amazon S3 bucket, which will incur additional costs and complexity for storing and analyzing log files that may not contain relevant information about secret usage. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will use AWS X-Ray to trace secret usage, which will introduce additional overhead and latency for instrumenting and sampling requests that may not be related to secret usage.

References: [AWS Config Managed Rules], [Amazon EventBridge]

AWS Serverless Application Model (AWS SAM) is an open-source framework that enables developers to build and deploy serverless applications on AWS. AWS SAM uses a template specification that extends AWS CloudFormation to simplify the definition of serverless resources such as API Gateway, DynamoDB, and Lambda. The developer can use AWS SAM to define serverless resources in YAML and deploy them using the AWS SAM CLI.

References:

[What Is the AWS Serverless Application Model (AWS SAM)? - AWS Serverless Application Model]

[AWS SAM Template Specification - AWS Serverless Application Model]

The sam local start-api subcommand allows you to run your serverless application locally for quick development and testing1. It creates a local HTTP server that acts as a proxy for API Gateway and invokes your Lambda functions based on the AWS SAM template1. You can use the sam local start-api subcommand to test your REST API locally by sending HTTP requests to the local endpoint1.

This solution meets the requirements with the least amount of configuration because it uses a feature of AWS CDK that allows custom logic to be executed during stack deployment or deletion. The AWS Cloud Development Kit (AWS CDK) is a software development framework that allows you to define cloud infrastructure as code and provision it through CloudFormation. An AWS CDK custom resource is a construct that enables you to create resources that are not natively supported by CloudFormation or perform tasks that are not supported by CloudFormation during stack deployment or deletion. The developer can write a handler function in the code that uses AWS SDK calls to check for and delete unused resources, and create an AWS CDK custom resource that attaches the function code to a Lambda function and invokes it when the deployment stack runs. This way, the developer can automate the cleanup process without requiring additional configuration or integration. Creating a CloudFormation template from a JSON file will require additional configuration and integration with the central AWS CDK application. Creating an API in AWS Amplify will require additional configuration and integration with the central AWS CDK application and may not provide optimal performance or availability. Writing a handler function in the AWS Lambda console will require additional configuration and integration with the central AWS CDK application.

AWS STS AssumeRole: The central operation for assuming temporary security credentials, commonly used for cross-account access.

MFA Integration: The AssumeRole call can include MFA information to enforce multi-factor authentication.

Credentials for S3 Access: The returned temporary credentials would provide the necessary permissions to access the S3 bucket in the other account.

References:

AWS STS AssumeRole Documentation: https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html

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

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:In this scenario, the developer is configuring a mock integration in API Gateway. The integration request mapping template allows you to map the incoming request data to a format that the API expects. For mock integration, it's common to return specific HTTP status codes based on the conditions.

Using $context.integration.status: The $context.integration.status variable refers to the status of the API Gateway integration, which is useful for generating responses based on the condition. Option D correctly uses this variable to determine the HTTP status code, returning 200 for a successful mock request or 500 for a failure.

Alternatives:

Options A, B, and C do not use the correct context variables for handling mock integrations. These options would not return the correct status codes based on the actual integration status.

Scenario: Application needs to fetch songs and artists efficiently in a single operation.

BatchGetItem: This DynamoDB operation retrieves multiple items across different tables based on their primary keys in a single request.

Optimized for Request Batching: This approach reduces network traffic compared to performing multiple queries individually.

Data Modeling: The songs table is designed appropriately for this access pattern using artistName as the sort key.

References:

Amazon DynamoDB BatchGetItem: https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_BatchGetItem.ht

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.

References:

AWS Secrets Manager: https://aws.amazon.com/secrets-manager/

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

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.

References:

[ProvisionedThroughputExceededException - Amazon Kinesis Data Streams Service API Reference]

[Best Practices for Handling Kinesis Data Streams Errors]

Centralized Logging Benefits: Centralized logging is essential for operational visibility in scalable systems, especially those using multiple EC2 instances like our e-commerce website. CloudWatch provides this capability, along with other monitoring features.

CloudWatch Agent: This is the best way to send custom application logs from EC2 instances to CloudWatch. Here's the process:

Install the CloudWatch agent on each EC2 instance.

Configure the agent with a configuration file, specifying:

Which log files to collect.

The format in which to send logs to CloudWatch (e.g., JSON).

The specific CloudWatch Logs log group and log stream for these logs.

Viewing and Analyzing Logs: Once the agent is pushing logs, use the CloudWatch Logs console or API:

View and search the logs across all instances.

Set up alarms based on log events.

Use CloudWatch Logs Insights for sophisticated queries and analysis.

References:

Amazon CloudWatch Logs: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/WhatIsCloudWatchLogs.html

Unified CloudWatch Agent: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/AgentReference.html

CloudWatch Logs Insights: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/AnalyzingLogData.html

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.

This solution will limit the ability to download a premier content file in the S3 bucket to paid subscribers only because it uses a pre-signed object URL that grants temporary access to an S3 object for a specified duration. The pre-signed object URL can be generated by the company’s website when a paid subscriber requests a download, and can be verified by Amazon S3 using the signature in the URL. Option A is not optimal because it will allow anyone to download the content from the S3 bucket without verifying their subscription status. Option C is not optimal because it will require additional steps and costs to configure multi-factor authentication for accessing the S3 bucket objects, which may not be feasible or user-friendly for paid subscribers. Option D is not optimal because it will not prevent non-paying website visitors from accessing the S3 bucket objects, but only encrypt them at rest.

References: Share an Object with Others, [Using Amazon S3 Pre-Signed URLs]

Amazon S3 supports resource-based policies, which are JSON documents that specify the permissions for accessing S3 resources. A resource-based policy can be used to enforce encryption in transit by denying access to requests that do not use HTTPS. The condition key aws:SecureTransport can be used to check if the request was sent using SSL. If the value of this key is false, the request is denied; otherwise, the request is allowed. Reference: How do I use an S3 bucket policy to require requests to use Secure Socket Layer (SSL)?

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.

References:

IAM Roles for EC2: https://docs. aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-ec2.html

Generating Presigned URLs: https://docs.aws.amazon.com/AmazonS3/latest/userguide/ShareObjectPreSignedURL.htm

This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.

References: [DynamoDB Accelerator (DAX)], [Local Secondary Indexes]

The solution that will meet the requirements is to add a test phase to the amplify.yml build settings for the application. This way, the developer can run end-to-end tests on every code commit and catch any bugs before deploying to production. The other options either do not support end-to-end testing, or do not run tests automatically.

The X-Ray daemon is a software that collects trace data from the X-Ray SDK and relays it to the X-Ray service. The X-Ray daemon can run on any platform that supports Go, including Linux, Windows, and macOS. The developer can install and run the X-Ray daemon on the on-premises servers to capture and relay the data to the X-Ray service with minimal configuration. The X-Ray SDK is used to instrument the application code, not to capture and relay data. The Lambda function solutions are more complex and require additional configuration.

References:

[AWS X-Ray concepts - AWS X-Ray]

[Setting up AWS X-Ray - AWS X-Ray]

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.

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:When you need to provide an AWS Lambda function access to private resources in a VPC, the most common and straightforward approach is to attach the Lambda function to a VPC via private subnets. Once the Lambda function is associated with the VPC, you need to configure appropriate security groups to control the access to the private resources.

Lambda with VPC Access: Lambda functions can be attached to private subnets in a VPC, allowing them to access resources like RDS, EC2, or internal services within that VPC.

Security Groups: A security group acts as a virtual firewall for the Lambda function, ensuring that it can access only the necessary resources and ports in the VPC.

Alternatives:

Option B involves routing traffic through a VPN, which adds unnecessary complexity and operational overhead compared to simply attaching the Lambda to the VPC.

Option C requires configuring a VPC endpoint and a NAT gateway, which can be complex and costly.

Option D refers to AWS PrivateLink, which is used to access services over private connections, but it's unnecessary in this scenario unless you need a cross-VPC connection.

This solution allows the API to invoke the Lambda function asynchronously, which means that the API will return an immediate response without waiting for the function to complete. The X-Amz-Invocation-Type header specifies the invocation type of the Lambda function, and setting it to ‘Event’ means that the function will be invoked asynchronously. The function can then use Amazon Simple Email Service (SES) to send an email message when the report processing is complete.

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.

A function alias is a pointer to a specific Lambda function version. You can use aliases to create different environments for your function, such as development, testing, and production. You can also use aliases to perform blue/green deployments by shifting traffic between two versions of your function gradually. This way, you can easily roll back to a previous version if something goes wrong, without having to redeploy your code or change your configuration. Reference: AWS Lambda function aliases

DynamoDB TTL (Time-to-Live): A native feature that automatically deletes items after a specified expiration time.

Efficiency: Eliminates the need for scheduled deletion jobs, optimizing write throughput by avoiding potential throttling conflicts.

Seamless Integration: TTL works directly within DynamoDB, requiring minimal development overhead.

References:

DynamoDB TTL Documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html

Distributed Tracing with X-Ray: X-Ray helps visualize request paths and identify bottlenecks in applications distributed across Regions.

Region Annotations (Automatic for AWS Services): X-Ray automatically adds a Region annotation to segments representing calls to AWS services. This aids in tracing cross-Region traffic.

Region Annotations (Manual for User-Defined): For segments representing calls to user-defined services in different Regions, the developer needs to add the Region annotation manually to enable comprehensive tracing.

References:

AWS X-Ray: https://aws.amazon.com/xray/

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.

This solution will meet the requirements by allowing the Lambda functions to access the DB cluster securely within the same VPC without crossing the public internet. The developer can configure a VPC endpoint for RDS in a private subnet and assign it to the Lambda functions. The developer can also configure a security group for the Lambda functions that allows inbound traffic from the DB cluster on port 3306 (MySQL). Option A is not optimal because it will expose the DB cluster to public access, which may compromise its security and data integrity. Option B is not optimal because it will introduce additional latency and complexity to use an RDS database proxy for accessing the DB cluster from Lambda functions within the same VPC. Option C is not optimal because it will require additional costs and configuration to use a NAT gateway for accessing resources in private subnets from Lambda functions.

References: [Configuring a Lambda Function to Access Resources in a VPC]

AWS Secrets Manager is a service that helps securely store, rotate, and manage secrets such as API keys, passwords, and tokens. The developer can store the API credentials in AWS Secrets Manager and retrieve them at runtime by using the AWS SDK. This solution will meet the requirements of security, code management, and performance. Storing the API credentials in a local code variable or an S3 object is not secure, as it exposes the credentials to unauthorized access or leakage. Storing the API credentials in a DynamoDB table is also not secure, as it requires additional encryption and access control measures. Moreover, retrieving the credentials from S3 or DynamoDB may affect application performance due to network latency.

References:

[What Is AWS Secrets Manager? - AWS Secrets Manager]

[Retrieving a Secret - AWS Secrets Manager]

Instance Metadata Service: EC2 instances have access to an internal metadata service. It provides instance-specific information like instance ID, security groups, and public IP address.

Accessing Metadata:

Make an HTTP GET request to the base URL: http://169.254.169.254/latest/meta-data/

You'll get a list of available categories. The public IPv4 address is under public-ipv4.

References:

Instance Metadata and User Data: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html

Step Functions Retriers: Retriers provide a built-in way to gracefully handle transient errors within State Machines. Here's how to use them:

Directly attach a retrier to the problematic 'GetResource' task.

Configure the retrier:

ErrorEquals: Set this to ['TooManyRequestsException'] to target the specific error.

IntervalSeconds: Set to 10 for the desired retry delay.

MaxAttempts: Set to 1, as you want only one retry attempt.

Error Handling:

Upon 'TooManyRequestsException', the retrier triggers the task again after 10 seconds.

On a second failure, Step Functions moves to the next state or fails the workflow, as per your design.

'IllegalArgumentException' causes error propagation as intended.

References:

Error Handling in Step Functions: https://docs.aws.amazon.com/step-functions/latest/dg/concepts-error-handling.html

The UnprocessedKeys element indicates that the BatchGetItem operation did not process all of the requested items in the current response. This can happen if the response size limit is exceeded or if the table’s provisioned throughput is exceeded. To handle this situation, the developer should retry the batch operation with exponential backoff and randomized delay to avoid throttling errors and reduce the load on the table. The developer should also use an AWS SDK to make the requests, as the SDKs automatically retry requests that return UnprocessedKeys.

References:

[BatchGetItem - Amazon DynamoDB]

[Working with Queries and Scans - Amazon DynamoDB]

[Best Practices for Handling DynamoDB Throttling Errors]

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.

Principle of Least Privilege: Granting specific permissions through an IAM role is more secure than directly attaching policies to a function or using root user credentials.

IAM Roles for Lambda: Designed to provide temporary credentials to Lambda functions, enhancing security.

Reusability: The existing IAM policy ensures the correct S3 and DynamoDB access is granted.

References:

IAM Roles for Lambda Documentation: https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html

IAM Best Practices: https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html

AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. Secrets Manager enables you to store, retrieve, and rotate secrets such as database credentials, API keys, and passwords. Secrets Manager supports a secret type for RDS databases, which allows you to select an existing RDS database instance and generate credentials for it. Secrets Manager encrypts the secret using AWS Key Management Service (AWS KMS) keys and enables automatic rotation of the secret at a specified interval. A Lambda function can use the AWS SDK or CLI to retrieve the secret from Secrets Manager and use it to connect to the database. Reference: Rotating your AWS Secrets Manager secrets

Amazon API Gateway allows you to create, deploy, and manage a RESTful API to expose backend HTTP endpoints, AWS Lambda functions, or other AWS services1. You can use API Gateway to perform basic validation of an API request before proceeding with the integration request1. When the validation fails, API Gateway immediately fails the request, returns a 400 error response to the caller, and publishes the validation results in CloudWatch Logs1.

To test changes before deploying to a production environment, you can modify the existing API to add request validation and deploy the updated API to a new API Gateway stage1. This allows you to perform tests without affecting the production environment. Once testing is complete and successful, you can then deploy the updated API to the API Gateway production stage1.

This approach has the least operational overhead as it avoids unnecessary creation of new APIs or exporting and importing of APIs. It leverages the existing infrastructure and only requires changes in the configuration of the existing API1.

CloudFormation and Dynamic References: The DefinitionSubstitutions property in CloudFormation allows you to pass values into Step Functions state machines at runtime.

Cost-Effectiveness: This solution is cost-effective as it leverages CloudFormation's built-in capabilities, avoiding the need for additional services like Secrets Manager or AppConfig.

References:

AWS Step Functions State Machine: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-stepfunctions-statemachine.html

CloudFormation DefinitionSubstitutions: https://github.com/aws-cloudformation/aws-cloudformation-resource-providers-stepfunctions/issues/14

IAM Roles for EC2: IAM roles are the recommended way to provide AWS credentials to applications running on EC2 instances. Here's how this works:

You create an IAM role with the necessary permissions to access the target S3 bucket.

You create an instance profile and associate the IAM role with this profile.

When launching the EC2 instance, you attach this instance profile.

Temporary Security Credentials: When the application on the EC2 instance needs to access S3, it doesn't directly use access keys. Instead, the AWS SDK running on the instance retrieves temporary security credentials associated with the role. These are rotated automatically by AWS.

References:

IAM Roles for Amazon EC2: https://docs. aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-ec2.html

Temporary Security Credentials: https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp.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.

References: [AWS Secrets Manager], [Rotating Your AWS Secrets Manager Secrets]

Problem: Directory access without file names fails.

S3 Static Website Hosting:

Configuring S3 as a static website enables automatic serving of index.html for directory requests.

Bucket policies ensure correct access permissions.

Updating the CloudFront origin simplifies routing.

Avoiding Public Exposure: The S3 website endpoint allows CloudFront to access content without making the bucket public.

References:

S3 Static Website Hosting: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

This solution will meet the requirements by using the Time to Live (TTL) feature of DynamoDB, which enables automatically deleting items from a table after a certain time period. The developer can add a new attribute of type Number that has a timestamp that is set to 48 hours after the blog post creation time, which represents the expiration time of the item. The developer can configure the DynamoDB table with a TTL that references the new attribute, which instructs DynamoDB to delete the item when the current time is greater than or equal to the expiration time. This solution is also cost-effective as it does not incur any additional charges for deleting expired items. Option A is not optimal because it will create a script to find and remove old posts with a table scan and a batch write item API operation, which may consume more read and write capacity units and incur more costs. Option B is not optimal because it will use Amazon Elastic Container Service (Amazon ECS) and AWS Fargate to run the script, which may introduce additional costs and complexity for managing and scaling containers. Option C is not optimal because it will create a global secondary index (GSI) that uses the expiration time as a sort key, which may consume more storage space and incur more costs.

References: Time To Live, Managing DynamoDB Time To Live (TTL)

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.

References:

1: AWS SAM CLI configuration file - AWS Serverless Application Model

2: Configuration file basics - AWS Serverless Application Model

3: Specify a configuration file - AWS Serverless Application Model

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.

Lambda Layers: These are designed to package dependencies that you can share across functions.

How to Use:

Create a layer, upload your 100MB library as a zip.

Attach the layer to your function.

In your function code, import the library from the standard layer path.

References:

Lambda Layers: https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html

Amazon ElastiCache for Memcached is a fully managed, multithreaded, and scalable in-memory key-value store that can be used to cache frequently accessed data and improve application performance1. By using Amazon ElastiCache for Memcached, the developer can reduce the load on the main database and handle high traffic surges more efficiently.

To use Amazon ElastiCache for Memcached, the developer needs to create a cache cluster with one or more nodes, and configure the application to store and retrieve data from the cache cluster2. The developer can use any of the supported Memcached clients to interact with the cache cluster3. The developer can also use Auto Discovery to dynamically discover and connect to all cache nodes in a cluster4.

Amazon ElastiCache for Memcached is compatible with the Memcached protocol, which means that the developer can use existing tools and libraries that work with Memcached1. Amazon ElastiCache for Memcached also supports data partitioning, which allows the developer to distribute data among multiple nodes and scale out the cache cluster as needed.

Using Amazon ElastiCache for Memcached is a simple and effective solution that meets the requirements with the least complexity. The developer does not need to change the database schema, migrate data to a different service, or use a different caching model. The developer can leverage the existing Memcached ecosystem and easily integrate it with the application.

https://aws.amazon.com/blogs/devops/automated-code-review-on-pull-requests-using-aws-codecommit-and-aws-codebuild/

This solution will meet the requirements by creating a single S3 bucket policy that denies access to the S3 bucket unless the request comes from one of the specified VPC endpoints. The aws:SourceVpce condition key is used to match the ID of the VPC endpoint that is used to access the S3 bucket. The StringNotEquals condition operator is used to negate the condition, so that only requests from the listed VPC endpoints are allowed. Option A is not optimal because it will create multiple S3 bucket policies, which is not possible as only one bucket policy can be attached to an S3 bucket. Option B is not optimal because it will use the aws:SourceVpc condition key, which matches the ID of the VPC that is used to access the S3 bucket, not the VPC endpoint. Option C is not optimal because it will use the StringNotEquals condition operator with a single value, which will deny access to the S3 bucket from all VPC endpoints except one.

References: Using Amazon S3 Bucket Policies and User Policies, AWS Global Condition Context Keys

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.

References: AWS CodeBuild, AWS CodePipeline

This solution meets the requirements in the most secure way because it uses a service that is integrated with CloudFormation to manage sensitive data in encrypted form. AWS Systems Manager Parameter Store provides secure, hierarchical storage for configuration data management and secrets management. You can store sensitive data as secure string parameters, which are encrypted using an AWS Key Management Service (AWS KMS) key of your choice. You can also use dynamic references in your CloudFormation templates to specify template values that are stored in Parameter Store or Secrets Manager without having to include them in your templates. Dynamic references are resolved only during stack creation or update operations, which reduces exposure risks for sensitive data. Putting sensitive data into a CloudFormation parameter will not encrypt them or protect them from unauthorized access. Putting sensitive data into an Amazon S3 bucket or Amazon Elastic File System (Amazon EFS) will require additional configuration and integration with CloudFormation and may not provide fine-grained access control or encryption for sensitive data.

Amazon Elastic Block Store (Amazon EBS) provides block level storage volumes for use with Amazon EC2 instances1. Amazon EBS encryption offers a straight-forward encryption solution for your EBS resources associated with your EC2 instances1. When you create an encrypted EBS volume and attach it to a supported instance type, the following types of data are encrypted: Data at rest inside the volume, all data moving between the volume and the instance, all snapshots created from the volume, and all volumes created from those snapshots1. Therefore, option A is correct.

The HTTP header that the developer should use for this analysis is the X-Forwarded-For header. This header contains the IP address of the client that made the request to the Application Load Balancer. The developer can use this header to analyze patterns for the client IP addresses that use the application. The other headers either contain information about the protocol, host, or port of the request, which are not relevant for the analysis.

This solution will meet the requirements by using AWS X-Ray to enable tracing of application requests to debug performance issues in the code. AWS X-Ray is a service that collects data about requests that the applications serve, and provides tools to view, filter, and gain insights into that data. The developer can install the AWS X-Ray daemon on the EC2 instances, which is a software that listens for traffic on UDP port 2000, gathers raw segment data, and relays it to the X-Ray API. The developer can also install and configure the AWS X-Ray SDK for Python in the application, which is a library that enables instrumenting Python code to generate and send trace data to the X-Ray daemon. Option A is not optimal because it will install the Amazon CloudWatch agent on the EC2 instances, which is a software that collects metrics and logs from EC2 instances and on-premises servers, not application performance data. Option C is not optimal because it will configure the application to write JSON-formatted logs to /var/log/cloudwatch, which is not a valid path or destination for CloudWatch logs. Option D is not optimal because it will configure the application to write trace data to /var/log/xray, which is also not a valid path or destination for X-Ray trace data.

References: [AWS X-Ray], [Running the X-Ray Daemon on Amazon EC2]

https://docs.aws.amazon.com/cognito/latest/developerguide/amazon-cognito-integrating-user-pools-with-identity-pools.html

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.

References: Amazon ElastiCache, Amazon RDS

Amazon Simple Notification Service (Amazon SNS) is a fully managed messaging service that enables pub/sub communication between distributed systems. The developer can create an SNS topic and configure the Lambda function to publish messages with specific attributes to the topic. The developer can subscribe each partner to the SNS topic and apply the appropriate filter policy to the topic subscriptions. This way, each partner will receive updates for only their own orders based on the message attributes. This solution will meet the requirements in the most scalable way and allow adding new partners in the future with minimal code changes.

References:

[Amazon Simple Notification Service (SNS)]

[Filtering Messages with Attributes - Amazon Simple Notification Service]

This solution will meet the requirements by using a rolling with additional batch deployment method, which deploys the new version of the application to a separate group of instances and then shifts traffic to those instances in batches. This way, the application maintains full capacity and avoids service interruption during deployment, as well as minimizes the cost of additional resources that support the deployment. Option A is not optimal because it will use an all at once deployment method, which deploys the new version of the application to all instances simultaneously, which may cause service interruption or downtime during deployment. Option C is not optimal because it will use a blue/green deployment method, which deploys the new version of the application to a separate environment and then swaps URLs with the original environment, which may incur more costs for additional resources that support the deployment. Option D is not optimal because it will use an immutable deployment method, which deploys the new version of the application to a fresh group of instances and then redirects traffic to those instances, which may also incur more costs for additional resources that support the deployment.

References: AWS Elastic Beanstalk Deployment Policies

Increasing the number of shards of the Kinesis data stream will increase the throughput and parallelism of the data processing. Increasing the memory that is allocated to the Lambda function will also increase the CPU and network performance of the function, which will reduce the run duration and improve the processing speed. Option B is not correct because decreasing the timeout of the Lambda function will not affect the processing speed, but may cause some records to fail if they exceed the timeout limit. Option D is not correct because decreasing the number of shards of the Kinesis data stream will decrease the throughput and parallelism of the data processing, which will slow down the processing speed. Option E is not correct because increasing the timeout of the Lambda function will not affect the processing speed, but may increase the cost of running the function.

References: [Amazon Kinesis Data Streams Scaling], [AWS Lambda Performance Tuning]

Amazon API Gateway is a service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. Amazon CloudFront is a content delivery network (CDN) service that can improve the performance and security of web applications. The developer can use CloudFront and a custom domain name for the API Gateway REST API. To do so, the developer needs to import the SSL/TLS certificate into AWS Certificate Manager (ACM) in the us-east-1 Region. This is because CloudFront requires certificates from ACM to be in this Region. The developer also needs to create a DNS CNAME record for the custom domain that points to the CloudFront distribution.

References:

[What Is Amazon API Gateway? - Amazon API Gateway]

[What Is Amazon CloudFront? - Amazon CloudFront]

[Custom Domain Names for APIs - Amazon API Gateway]

This solution will meet the requirements by using AWS Encryption SDK, which is a client-side encryption library that enables developers to encrypt and decrypt data using data encryption keys that are protected by AWS Key Management Service (AWS KMS). The SDK encrypts the data encryption key with a customer master key (CMK) that is managed by AWS KMS, and stores it (encrypted) as part of the returned ciphertext. The developer does not need to keep track of the data encryption keys used to encrypt data, as they are stored with the encrypted data and can be retrieved and decrypted by using AWS KMS when needed. Option A is not optimal because it will require manual tracking of the data encryption keys used for each data object, which is error-prone and inefficient. Option C is not optimal because it will store the data encryption keys automatically in Amazon S3, which is unnecessary and insecure as Amazon S3 is not designed for storing encryption keys. Option D is not optimal because it will store the data encryption key in the user data for the EC2 instance, which is also unnecessary and insecure as user data is not encrypted by default.

References: [AWS Encryption SDK], [AWS Key Management Service]

Amazon Cognito user pools support Lambda triggers, which are custom functions that can be executed at various stages of the user pool workflow. A post authentication Lambda trigger can be used to perform custom actions after a user is authenticated, such as sending an email notification. Amazon SES is a cloud-based email sending service that can be used to send transactional or marketing emails. A Lambda function can use the Amazon SES API to send an email to the user’s email address after the user logs in successfully. Reference: Post authentication Lambda trigger