AWS Certified Data Engineer Associate DEA-C01 Practice Question
An organization needs to build a nightly batch pipeline that transfers data from an on-premises MySQL database to Amazon S3 in a format directly queryable by Amazon Athena. Only rows that have changed since the previous run should be moved to minimize transfer and storage costs. Which AWS DMS task configuration meets these requirements with the LEAST operational overhead?
Create an AWS DMS task that performs a full load followed by ongoing CDC to an Amazon S3 target endpoint configured to output Apache Parquet files.
Run a nightly mysqldump on the source database, transfer the dump to Amazon S3 with AWS DataSync, and query the files in Athena with a CSV SerDe.
Schedule a daily AWS DMS task that runs in full-load-only mode and overwrites the previous day's data in Amazon S3 using CSV format.
Replicate the MySQL tables to Amazon Redshift with AWS DMS, then run nightly UNLOAD commands to export the data to S3 in Parquet format.
AWS DMS can perform a full load of the source tables once and then keep replicating only the data changes by using change data capture (CDC). When the target endpoint is Amazon S3 and the output format is set to Apache Parquet, the files written can be queried natively by Athena. Leaving the task running continuously eliminates the need to re-create or re-schedule jobs and ensures that only incremental data is written, which keeps transfer and storage costs low. The other options either reload the entire dataset every day, introduce additional services and operational steps, or rely on manual dumps-none of which provides the required incremental, low-overhead solution.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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