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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Topic 2: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Topic 4: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
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NEW QUESTION # 26
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
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 AmazonAthena 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
NEW QUESTION # 27
A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically.
Which solution will meet these requirements?
Answer: D
Explanation:
Option B is correct because IAM policies are the standard AWS mechanism to control access to Amazon S3, and SSE-KMS encrypts S3 data with AWS KMS keys. AWS S3 documentation for SSE-KMS explains that it uses AWS Key Management Service keys for server-side encryption, and Secrets Manager documentation states that you can store secrets securely and configure automatic rotation. AWS further explains that rotation updates the credentials in both the secret and the target database or service, and that Secrets Manager uses a Lambda rotation function for supported rotation patterns.
Option A is weaker because Lambda environment variables are not the right service for managed secret storage and automatic rotation. Option C is not the best answer because S3 ACLs are not the preferred modern access-control model compared with IAM and bucket policies, and Parameter Store is not the main AWS service for built-in managed database credential rotation. Option D uses SSE-S3, not KMS-based encryption, and relies on a custom rotation approach instead of the native managed secret-rotation service.
The study guide also highlights AWS KMS for encryption-key management and Secrets Manager for rotating credentials.
NEW QUESTION # 28
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G.1X worker type.
Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request." What is the likely root cause of the error?
Answer: C
Explanation:
Option A is correct because when many AWS Glue jobs start at the same time in the same subnet, Glue needs to create network interfaces in that subnet. If the subnet does not have enough free private IP addresses available, jobs can fail with exactly this type of error. AWS service documentation for VPC-based managed data services consistently notes that if there is no available free IP address in a specified subnet, the service cannot create or add the required ENIs and the workload can fail or degrade.
This is a subnet-level exhaustion issue, not a VPC-wide issue. A VPC can still have free addresses in other subnets while the specific subnet chosen for the jobs is out of usable addresses. That makes D incorrect.
Option B is incorrect because G.1X is a valid Glue worker type and does not inherently prevent subnet access.
Option C is also incorrect because the error is about address availability, not IAM authorization. In practice, this problem is usually resolved by using a larger subnet or spreading workloads across additional subnets so enough IPs are available when multiple Glue jobs launch concurrently. This aligns with the exam's focus on networking capacity planning for managed data services.
NEW QUESTION # 29
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. Reference:
AWS Glue
Working with Python Shell Jobs
pandas
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 30
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: D,E
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 # 31
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