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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
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NEW QUESTION # 228
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows.
You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.
The other options are not optimal for the following reasons:
* B. AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.
* C. AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.
* D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs).
You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows.
However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.
References:
* 1: AWS Glue Workflows
* 2: AWS Glue and Amazon EMR
* 3: AWS Step Functions
* : AWS Lambda
* : Amazon Managed Workflows for Apache Airflow
NEW QUESTION # 229
A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
Answer: B,C
Explanation:
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.
:
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
NEW QUESTION # 230
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?
Answer: D
Explanation:
* Problem Analysis:
* The company needs to migrate both anapplicationand anon-premises Apache Kafka serverto AWS.
* Incremental updates from an on-premises Oracle database are processed by Kafka.
* The solution must follow areplatform migration strategy, prioritizing minimal changes andlow management overhead.
* Key Considerations:
* Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
* The solution must provide amanaged Kafka serviceto minimize operational burden.
* Low overhead solutions like serverless services are preferred.
* Solution Analysis:
* Option A: Kinesis Data Streams
* Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
* This option would require significant application refactoring, which does not align with the replatform strategy.
* Option B: MSK Provisioned Cluster
* Managed Kafka service with fully configurable clusters.
* Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
* Option C: Amazon Kinesis Data Firehose
* Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
* Not suitable for Kafka-based applications.
* Option D: MSK Serverless
* MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
* Automatically scales based on workload, reducing operational overhead.
* Ideal for replatform migrations, as it requires minimal changes to the application.
* Final Recommendation:
* Amazon MSK Serverlessis the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis
NEW QUESTION # 231
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
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
NEW QUESTION # 232
A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run. The company has noticed that the queries cause table-level locks during the ETL operations. The company ' s current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?
Answer: D
Explanation:
Option D is correct because the problem is lock contention between ETL writes and analytical reads on the same Redshift tables. AWS documents that table locks can block both reads and writes, and that Redshift data sharing lets you share live data across Redshift clusters and workgroups without manually moving or copying the data. Because the shared data is live, consumer users see the most up-to-date information as soon as it is updated. Separating ETL and analytics onto different Redshift clusters while using data sharing is therefore the cleanest way to isolate workloads and still provide consistent data views.
Option A reduces contention only by changing schedules and does not fully solve concurrent peak-hour conflicts. Option B is not correct because Redshift federated queries are read-only against external sources and do not provide a Redshift "read replica" design for this case. Option C does not address the locking problem on the dimension tables that ETL is updating. The official Redshift guidance and service capabilities point to workload isolation plus data sharing as the best fix. This also matches the exam guide's emphasis on choosing architectures that maintain performance and consistency without unnecessary copying.
NEW QUESTION # 233
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