Score Higher on Verified Data-Engineer-Associate | AWS Certified Data Engineer - Associate (DEA-C01) Exam Questions with Answers

 An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.

The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. References:

What is a data lake?

Choosing a data format for your data lake

Using the MERGE INTO command in Delta Lake

[AWS Lambda quotas]

[AWS Database Migration Service quotas]

Amazon Athena Federated Query enables SQL queries across multiple data sources without moving data. Athena can directly query Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 using data source connectors. This capability is ideal for one-time or ad hoc analytical queries, where setting up ingestion pipelines would introduce unnecessary cost and delay.

Because the requirement is for a one-time performance report, the solution should minimize operational overhead and data duplication. Athena Federated Query executes queries in place, eliminating ETL processes, data exports, and staging layers. This significantly reduces query preparation time and avoids storage costs associated with copying data into a centralized location.

The other options require extensive data movement and setup. DynamoDB Streams, AWS DMS, Glue ETL pipelines, and Amazon EMR clusters are appropriate for ongoing analytics pipelines, not for a single analytical report. These approaches increase operational complexity and execution time.

Athena Federated Query is purpose-built for this exact scenario and provides the fastest path to insight with minimal overhead.

The best solution for fast, event-driven processing of unpredictable file uploads is to use S3 event notifications, CloudTrail, and EventBridge to automatically trigger the AWS Glue workflow:

“You can configure S3 PutObject events to be captured by CloudTrail and forwarded through EventBridge to trigger an AWS Glue job or workflow. This allows Glue to begin processing as soon as the file arrives, with minimal latency.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This option provides the lowest latency and least manual overhead compared to polling or scheduling solutions.