Data Operations and Support 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 media company is migrating an on-premises SQL Server database to Amazon Aurora PostgreSQL with an AWS DMS task that runs full load and ongoing change data capture.
Before the cutover, auditors want evidence that every migrated row matches the source, including rows changed during replication. The team wants the check to run automatically and to name any rows that differ, with little custom code.
Which solution meets these requirements?
Choose one answer.
AA. Run a nightly script that counts the rows in each source and target table, and compare the totals in a shared spreadsheet.
BB. Run an AWS Glue crawler against both databases, and compare the table schemas in the two catalogs after each replication run.
CC. Turn on data validation for the DMS task, and review its validation state and the awsdms_validation_failures_v1 table on the target.
DD. Add a Glue Data Quality ruleset with a RowCount rule to the target tables, and evaluate it after each change data capture batch.
Show the answer and explanation
Answer: C
A. Wrong: Matching counts do not prove that the values match, and the script cannot name the rows that differ. It is also custom code that the team would maintain.
B. Wrong: A crawler records table schemas, not row values. Two tables can have the same schema and still hold different data, so this gives no evidence about the rows.
C. Right: Correct. DMS data validation compares each source row with its target row after the full load and as changes replicate. Rows that fail are recorded with their keys and differing values in the validation failures table on the target. Learn more: AWS DMS data validation (opens in a new tab)
D. Wrong: The rule checks only the target and only how many rows it holds. It cannot compare values with the source, so mismatched rows would pass.
Explanation
A migration check must compare values, not just counts or schemas. DMS data validation compares source and target rows during full load and change data capture, and it records each failed row with its key and the values that differ. Count scripts, crawlers and a target-only RowCount rule cannot show that every row matches.
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