Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

The best solution to cost optimize the company’s use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of Amazon Athena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31. This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.

Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company’s use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.

Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company’s use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.

Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company’s use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. References:

Query result reuse

Amazon S3 Lifecycle

S3 Glacier Deep Archive

Storage classes supported by Athena

[What is Amazon ElastiCache?]

[Amazon Athena pricing]

[Columnar Storage Formats]

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

The data engineer needs to orchestrate ETL jobs that include Spark jobs on Amazon EMR, API calls to Salesforce, and loading data into Redshift. They also need automatic failure handling and retries. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the best solution for this requirement.

Option A: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)Apache Airflow is designed for complex job orchestration, allowing users to define workflows (DAGs) in Python. MWAA manages Airflow and its integrations with other AWS services, including Amazon EMR, Redshift, and external APIs like Salesforce. It provides automatic retry handling, failure detection, and detailed monitoring, which fits the use case perfectly.

Option B (AWS Step Functions) can orchestrate tasks but doesn't natively support complex workflow definitions with Python like Airflow does.

Option C (AWS Glue) is more focused on ETL and doesn't handle the orchestration of external systems like Salesforce as well as Airflow.

Option D (Amazon EventBridge) is more suited for event-driven architectures rather than complex workflow orchestration.

References:

Amazon Managed Workflows for Apache Airflow

Apache Airflow on AWS

 AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS as well as moving data to and from on-premises storage, edge locations, other cloud providers, and AWS Storage services1. AWS DataSync can copy data to and from various sources and targets, including Amazon S3, and handle files in multiple formats. AWS DataSync also supports incremental transfers, meaning it can detect and copy only the changes to the data, reducing the amount of data transferred and improving the performance. AWS DataSync can automate and schedule the transfer process using triggers, and monitor the progress and status of the transfers using CloudWatch metrics and events1.

AWS DataSync is the most operationally efficient way to transfer the data in this scenario, as it meets all the requirements and offers a serverless and scalable solution. AWS Glue, AWS Direct Connect, and Amazon S3 Transfer Acceleration are not the best options for this scenario, as they have some limitations or drawbacks compared to AWS DataSync. AWS Glue is a serverless ETL service that can extract, transform, and load data from various sources to various targets, including Amazon S32. However, AWS Glue is not designed for large-scale data transfers, as it has some quotas and limits on the number and size of files it can process3. AWS Glue also does not support incremental transfers, meaning it would have to copy the entire data set every time, which would be inefficient and costly.

AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises data center and AWS, bypassing the public internet and improving the bandwidth and performance of the data transfer. However, AWS Direct Connect is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS DataSync, AWS Storage Gateway, or AWS CLI. AWS Direct Connect also has some hardware and location requirements, and charges you for the port hours and data transfer out of AWS.

Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to Amazon S3 over long distances, using the AWS edge locations and optimized network paths. However, Amazon S3 Transfer Acceleration is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS CLI, AWS SDK, or third-party software. Amazon S3 Transfer Acceleration also charges you for the data transferred over the accelerated endpoints, and does not guarantee a performance improvement for every transfer, as it depends on various factors such as the network conditions, the distance, and the object size. References:

AWS DataSync

AWS Glue

AWS Glue quotas and limits

[AWS Direct Connect]

[Data transfer options for AWS Direct Connect]

[Amazon S3 Transfer Acceleration]

[Using Amazon S3 Transfer Acceleration]

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

References:

Amazon S3 Lifecycle Policies

S3 Storage Classes

Cost Management and Data Optimization Using Lifecycle Policies

AWS Data Engineering Documentation

AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue Studio is a graphical interface that allows you to easily author, run, and monitor AWS Glue ETL jobs. AWS Glue Data Quality is a feature that enables you to validate, cleanse, and enrich your data using predefined or custom rules. AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows.

Using the Detect PII transform in AWS Glue Studio, you can automatically identify and label the PII in your dataset, such as names, addresses, phone numbers, email addresses, etc. You can then create a rule in AWS Glue Data Quality to obfuscate the PII, such as masking, hashing, or replacing the values with dummy data. You can also use other rules to validate and cleanse your data, such as checking for null values, duplicates, outliers, etc. You can then use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. You can use AWS Glue DataBrew to visually explore and transform the data, AWS Glue crawlers to discover and catalog the data, and AWS Glue jobs to load the data into the S3 data lake.

This solution will meet the requirement with the least operational effort, as it leverages the serverless and managed capabilities of AWS Glue, AWS Glue Studio, AWS Glue Data Quality, and AWS Step Functions. You do not need to write any code to identify or obfuscate the PII, as you can use the built-in transforms and rules in AWS Glue Studio and AWS Glue Data Quality. You also do not need to provision or manage any servers or clusters, as AWS Glue and AWS Step Functions scale automatically based on the demand.

The other options are not as efficient as using the Detect PII transform in AWS Glue Studio, creating a rule in AWS Glue Data Quality, and using an AWS Step Functions state machine. Using an Amazon Kinesis Data Firehose delivery stream to process the dataset, creating an AWS Lambda transform function to identify the PII, using an AWS SDK to obfuscate the PII, and setting the S3 data lake as the target for the delivery stream will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. Using the Detect PII transform in AWS Glue Studio to identify the PII, obfuscating the PII, and using an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake will not be as effective as creating a rule in AWS Glue Data Quality to obfuscate the PII, as you will need to manually obfuscate the PII after identifying it, which can be error-prone and time-consuming. Ingesting the dataset into Amazon DynamoDB, creating an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data, and using the same Lambda function to ingest the data into the S3 data lake will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. You will also incur additional costs and complexity by using DynamoDB as an intermediate data store, which may not be necessary for your use case. References:

AWS Glue

AWS Glue Studio

AWS Glue Data Quality

[AWS Step Functions]

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.

AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.

Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.

Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.

Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.

Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.

Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:

What is Amazon Athena?

Data Catalog and crawlers in AWS Glue

AWS Glue Data Catalog

Columnar Storage Formats

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

AWS Glue Schema Registry

What is AWS Glue?

Amazon Redshift Serverless

Amazon Redshift provisioned clusters

[Querying external data using Amazon Redshift Spectrum]

[Using stored procedures in Amazon Redshift]

[What is AWS Lambda?]

[PartiQL for Amazon Athena]

[Federated queries in Amazon Athena]

[Amazon Athena pricing]

[Top 10 performance tuning tips for Amazon Athena]

[AWS Glue ETL jobs]

[AWS Lake Formation jobs]

Option A and B are the correct answers because they meet the requirements most cost-effectively. Using an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically is a simple and scalable way to orchestrate the queries. Creating an AWS Step Functions workflow and adding two states to check the query status and invoke the next query is a reliable and efficient way to handle the long-running queries.

Option C is incorrect because using an AWS Glue Python shell job to invoke the Athena queries programmatically is more expensive than using a Lambda function, as it requires provisioning and running a Glue job for each query.

Option D is incorrect because using an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully is not a cost-effective or reliable way to orchestrate the queries, as it wastes resources and time.

Option E is incorrect because using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch is an overkill solution that introduces unnecessary complexity and cost, as it requires setting up and managing an Airflow environment and an AWS Batch compute environment.

References:

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.2: AWS Lambda, Section 5.3: AWS Step Functions, Pages 125-135

Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.1: AWS Lambda, Lesson 5.2: AWS Step Functions, Pages 1-15

AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon Athena, Pages 1-4

AWS Documentation Overview, AWS Step Functions Developer Guide, Getting Started, Tutorial: Create a Hello World Workflow, Pages 1-8

AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.

The other options are not optimal for the following reasons:

B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .

References:

1: AWS Data Exchange User Guide

2: AWS Data Exchange FAQs

3: AWS Glue Developer Guide

: AWS CodeCommit User Guide

: Amazon Kinesis Data Streams Developer Guide

: Amazon Elastic Container Registry User Guide

: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source

 Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:

Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)

Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)

Monitoring with AWS Glue job run insights (Section: Requirements)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)

Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze streaming data in real time. You can use Kinesis Data Streams to capture sensor data from various sources, such as IoT devices, web applications, or mobile apps. You can create data streams that can scale up to handle any amount of data from thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis Data Streams API to write applications that process and analyze the data in the streams1.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use DynamoDB to store the sensor data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data, such as using key-value lookups, scans, or queries2.

The solution that meets the requirements with the least operational overhead is to use Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon DynamoDB for querying. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers, clusters, or queues, as Kinesis Data Streams and DynamoDB are fully managed services that handle all the infrastructure for you. This reduces the operational complexity and cost of running your solution.

It allows you to ingest sensor data in near real time, as Kinesis Data Streams can capture data records as they are produced and deliver them to your applications within seconds. You can also use Kinesis Data Firehose to load the data from the streams to DynamoDB automatically and continuously3.

It allows you to store the data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB Streams to capture changes in the data and trigger actions, such as sending notifications or updating other databases.

It allows you to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can also use DynamoDB Accelerator (DAX) to improve the read performance by caching frequently accessed data.

Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to capture the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own Kafka cluster, either on EC2 instances or on-premises servers. This increases the operational complexity and cost of running your solution.

It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.

Option B is incorrect because it suggests using AWS Lambda to process the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:

It does not allow you to ingest sensor data in near real time, as Lambda is a serverless compute service that runs code in response to events. You need to use another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda functions with sensor data, which may add extra latency and complexity to your solution.

It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.

Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS for querying. This solution has the following disadvantages:

It does not allow you to ingest sensor data in near real time, as Amazon SQS is a message queue service that delivers messages in a best-effort manner. You need to use another service, such as Lambda or EC2, to poll the messages from the queue and process them, which may add extra latency and complexity to your solution.

It does not allow you to store the data in nested JSON format, as Amazon RDS is a relational database service that supports structured data types, such as tables and columns. You need to use another service, such as AWS Glue, to transform the data from JSON to relational format, which may add extra cost and overhead to your solution.

References:

1: Amazon Kinesis Data Streams - Features

2: Amazon DynamoDB - Features

3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data Firehose

[4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB

[5]: Amazon DynamoDB Accelerator (DAX) - Features

[6]: Amazon S3 - Features

[7]: AWS Lambda - Features

[8]: Amazon Simple Queue Service - Features

[9]: Amazon Relational Database Service - Features

[10]: Working with JSON in Amazon RDS - Amazon Relational Database Service

[11]: AWS Glue - Features

The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.

Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.

Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.

Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.

Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.

References:

Amazon Redshift ML Documentation

Invoking SageMaker Endpoints from SQL

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.

Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.

Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.

The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.

It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.

It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.

Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.

Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.

Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:

It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.

It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.

References:

1: AWS Lambda - Features

2: Amazon Elastic Container Service - Features

3: Amazon Elastic Kubernetes Service - Features

[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway

[5]: Improving latency with Provisioned Concurrency - AWS Lambda

[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service

[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service

[8]: Managing concurrency for a Lambda function - AWS Lambda

 AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:

AWS Glue

[AWS Glue Data Catalog]

[AWS Glue Developer Guide]

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.

Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.

Other options:

AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.

AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.

AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.

References:

Amazon Managed Workflows for Apache Airflow (MWAA) Documentation

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

References:

Amazon Redshift COPY Command Documentation

The telecommunications company needs a low-latency solution to detect sudden drops in network usage from real-time data collected throughout the day.

Option B: Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (Amazon Kinesis Data Analytics) application to detect drops in network usage.Using Amazon Kinesis with Managed Service for Apache Flink (formerly Kinesis Data Analytics) is ideal for real-time stream processing with minimal latency. Flink can analyze the incoming data stream in real-time and detect anomalies, such as sudden drops in usage, which makes it the best fit for this scenario.

Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases) or do not provide the same real-time, low-latency processing that is critical for this use case.

References:

Amazon Kinesis Data Analytics for Apache Flink

Amazon Kinesis Documentation

The requirement involves transforming CSV files by renaming columns, removing rows, and other operations with minimal development effort. AWS Glue DataBrew is the best solution here because it allows you to visually create transformation recipes without writing extensive code.

Option D: Use AWS Glue DataBrew recipes to read and transform the CSV files.DataBrew provides a visual interface where you can build transformation steps (e.g., renaming columns, filtering rows, creating new columns, etc.) as a "recipe" that can be applied to datasets, making it easy to handle complex transformations on CSV files with minimal coding.

Other options (A, B, C) involve more manual development and configuration effort (e.g., writing Python jobs or creating custom workflows in Glue) compared to the low-code/no-code approach of DataBrew.

References:

AWS Glue DataBrew Documentation

 Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.

Therefore, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.

The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:

Amazon Athena

Choosing the Right Data Format

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provide object-level tracking for S3 to capture detailed API actions on specific S3 objects, including the user who performed the action.

AWS CloudTrail:

CloudTrail can monitor API calls made to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.

CloudTrail logs include details such as the user identity, which is essential for meeting the requirement of including the username in notifications.

The CloudTrail logs can be forwarded to Amazon CloudWatch to trigger alarms based on certain access patterns (e.g., violations of specific policies).

 AWS Glue is a fully managed serverless ETL service that can handle data deduplication with minimal operational overhead. AWS Glue provides a built-in ML transform called FindMatches, which can automatically identify and group similar records in a dataset. FindMatches can also generate a primary key for each group of records and remove duplicates. FindMatches does not require any coding or prior ML experience, as it can learn from a sample of labeled data provided by the user. FindMatches can also scale to handle large datasets and optimize the cost and performance of the ETL job. References:

AWS Glue

FindMatches ML Transform

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

 The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.

AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entire table metadata. This reduces the query planning time and improves the query performance1.

Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to ‘project’, or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.

Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.

Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.

Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:

Improve query performance using AWS Glue partition indexes

Partition projection with Amazon Athena

Bucketing vs Partitioning

Columnar Storage Formats

S3DistCp

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Option A is the correct answer because it meets the requirements with the least operational overhead. Creating an S3 event notification that has an event type of s3:ObjectCreated:* will trigger the Lambda function whenever a new object is created in the S3 bucket. Using a filter rule to generate notifications only when the suffix includes .csv will ensure that the Lambda function only runs for .csv files. Setting the ARN of the Lambda function as the destination for the event notification will directly invoke the Lambda function without any additional steps.

Option B is incorrect because it requires the user to tag the objects with .csv, which adds an extra step and increases the operational overhead.

Option C is incorrect because it uses an event type of s3:*, which will trigger the Lambda function for any S3 event, not just object creation. This could result in unnecessary invocations and increased costs.

Option D is incorrect because it involves creating and subscribing to an SNS topic, which adds an extra layer of complexity and operational overhead.

References:

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.2: S3 Event Notifications and Lambda Functions, Pages 67-69

Building Batch Data Analytics Solutions on AWS, Module 4: Data Transformation, Lesson 4.2: AWS Lambda, Pages 4-8

AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon S3, Pages 1-5

The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use a manifest file.

Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.

Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.

References:

Amazon Redshift COPY Command

Redshift Manifest File Documentation

 AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.

AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.

AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.

Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:

AWS Glue Workflows

AWS Step Functions

AWS Glue Studio

Amazon MWAA

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. References:

Query editor V2

Working with materialized views

Scheduling queries

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.

Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.

Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.

References:

AWS Glue Crawler Documentation

AWS Glue Classifiers

The AWS Transfer Family server's security policy can be updated to enforce TLS 1.2 or higher, ensuring compliance with company policy for encrypted data transfers.

AWS Transfer Family Security Policy:

AWS Transfer Family supports setting a minimum TLS version through its security policy configuration. This ensures that only connections using TLS 1.2 or above are allowed.

The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).

Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.

Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.

References:

DynamoDB Time-to-Live (TTL)

Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes. References:

Working with concurrency scaling - Amazon Redshift

Amazon Redshift Concurrency Scaling - Amazon Web Services

Configuring concurrency scaling queues - Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)

AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.

Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.

The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:

AWS Step Functions

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions

The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:

AWS Glue triggers

AWS Glue connections

AWS Glue DataBrew

[AWS Lambda functions]

[Redshift Data API]

For secure migration of data from an on-premises data center to AWS without using the public internet, AWS Direct Connect is the most secure and reliable method. Using Secrets Manager to store service account credentials ensures that the credentials are managed securely with automatic rotation.

AWS Direct Connect:

Direct Connect establishes a dedicated, private connection between the on-premises data center and AWS, avoiding the public internet. This is ideal for secure, high-speed data transfers.

The company wants to ensure that no customer records are deleted or modified for 7 years, and even the root user should not have the ability to change the data. S3 Object Lock in Compliance Mode is the correct solution for this scenario.

Option B: Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.In Compliance Mode, even the root user cannot delete or modify locked objects during the retention period. This ensures that the data is protected for the entire 7-year duration as required. Compliance mode is stricter than governance mode and prevents all forms of alteration, even by privileged users.

Option A (Governance Mode) still allows certain privileged users (like the root user) to bypass the lock, which does not meet the company's requirement. Option C (legal hold) and Option D (setting retention per object) do not fully address the requirement to block root user modifications.

References:

Amazon S3 Object Lock Documentation

The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.

AWS Certificate Manager (ACM):

ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.

ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.

 Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.

The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:

Choosing a data distribution style

Choosing a data sort key

Working with primary keys

This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. References:

Amazon Managed Service for Apache Flink

Apache Flink

Window Aggregations in Flink