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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% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Topic 3: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 4: Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
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NEW QUESTION # 165
A hotel management company receives daily data files from each of its hotels. The company wants to upload its data to AWS. The company plans to use Amazon Athena to access the files. The company needs to protect the files from accidental deletion. The company will develop an application on its on-premises servers to automatically forward the files to a fully managed AWS ingestion service.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon Kinesis Data Firehose is a fully managed data ingestion service that enables reliable and scalable delivery of streaming and batch data into Amazon S3 with minimal operational overhead. This directly satisfies the requirement for a fully managed AWS ingestion service while avoiding the need to provision, scale, or manage infrastructure.
By using the Amazon Kinesis Agent on the on-premises servers, the company can automatically forward daily data files to Kinesis Data Firehose. Firehose handles buffering, retry logic, scaling, and delivery without requiring administrative effort. Delivering the data to Amazon S3 allows seamless integration with Amazon Athena, which natively queries data stored in S3 without requiring data movement or transformation.
Enabling Amazon S3 versioning protects files from accidental deletion by preserving previous versions of objects. This aligns with AWS best practices for data durability and governance, especially for analytics workloads and compliance requirements.
Other options introduce unnecessary operational complexity. AWS DataSync with Amazon EFS is not optimized for Athena-based analytics. AWS Glue jobs and Amazon RDS are unsuitable for file-based analytical access. A self-managed Apache Kafka solution with Amazon MSK significantly increases operational overhead.
Therefore, option B is the most efficient, scalable, and operationally optimal solution according to AWS Certified Data Engineer - Associate best practices.
NEW QUESTION # 166
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)
Answer: A,E
Explanation:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
Reference:
Amazon Redshift Documentation - Sorting Data
AWS Certified Data Analytics Study Guide
AWS Certification - Data Engineer Associate Exam Guide
NEW QUESTION # 167
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real- time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results. References:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 168
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?
Answer: C
Explanation:
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
Option D: YourEnvironmentName-Task
Task logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
Reference:
Amazon MWAA Logging and Monitoring
Apache Airflow Task Logs
NEW QUESTION # 169
A company has an application that is deployed on AWS. The application uses Amazon Simple Notification Service (Amazon SNS) with multiple topics. The company's security team needs to be able to audit all Publish and PublishBatch API actions for all the SNS topics. The company ' s application team and security team must also be able to query the audit data. The company has already established an event data store in AWS CloudTrail Lake to collect all events.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
The correct answer is C because SNS Publish and PublishBatch operations are data-plane operations, not ordinary management events. AWS CloudTrail documentation specifically lists Amazon SNS Publish and PublishBatch API operations on topics as supported data events. Because the company already has a CloudTrail Lake event data store, the lowest-operational-overhead approach is to enable the relevant SNS data events and query them directly in CloudTrail Lake. Creating Glue Data Catalog tables and querying with Athena adds unnecessary cataloging and query infrastructure. Management events would not satisfy the requirement to audit the requested SNS publishing actions. Therefore, the precise configuration is SNS data events plus CloudTrail Lake querying.
NEW QUESTION # 170
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