DEA-C01 practice question: Batch ingestion and migration with DMS and AppFlow (1 of 2)
Data Ingestion and Transformation A free question from our original DEA-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A dental chain keeps patient-appointment records in an on-premises Oracle database. The analytics team needs a copy in Amazon S3 that reflects inserts, updates and deletes within about 15 minutes, and the full table must be loaded first.
The source is busy during clinic hours, so the team wants a migration service that reads the database's change log rather than running full-table queries every few minutes. The output should be files that a later pipeline can process.
Which solution meets these requirements?
Choose one answer.
AA. Run an AWS Glue crawler every 15 minutes against the Oracle database, and write the results to S3 as Parquet files.
BB. Create an AWS DMS task with full load and change data capture from Oracle, writing changes to an S3 target as files.
CC. Export the Oracle tables to CSV every night, then copy the files to S3 with AWS DataSync.
DD. Run an AWS Lambda function on a five-minute timer that runs SELECT * on each table and writes the rows to S3.
Show the answer and explanation
Answer: B
A. Wrong: A crawler reads schema metadata to build catalog tables and does not copy row data from the database. It would not capture updates or deletes, so it cannot meet the freshness requirement.
B. Right: Correct. A DMS full-load-and-CDC task loads the existing table, then reads the Oracle change log and applies inserts, updates and deletes to the target. The S3 target writes those changes as files that a later pipeline can process. Learn more: What is AWS Database Migration Service? (opens in a new tab)
C. Wrong: A nightly export misses the 15-minute freshness target. Full-table exports also add heavy read load to the source database.
D. Wrong: Full-table queries every few minutes add heavy load to the busy source, and they cannot see rows that were deleted. The approach fails both the change-tracking and the load requirements.
Explanation
DMS full-load-and-CDC tasks handle both the initial copy and the ongoing changes, reading the database's change log rather than querying tables. The S3 target delivers the changes as files for later processing. Crawlers, nightly exports and polling queries each miss the freshness goal or load the busy source.
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