A company wants to migrate its existing on-premises Oracle database to Amazon Aurora PostgreSQL. The migration must be completed with minimal downtime using AWS DMS. A Database Specialist must…

Question

Options

• AUse the AWS Schema Conversion Tool (AWS SCT) to convert source Oracle database schemas
• BUse the table metrics of the AWS DMS task created for migrating the data to verify the statistics
• CEnable the AWS Schema Conversion Tool (AWS SCT) premigration validation and review the
• DEnable AWS DMS data validation on the task so the AWS DMS task compares the source and

• A
  5% (3)
• B
  13% (8)
• C
  9% (6)
• D
  73% (47)

Explanation

Explanation

Option D is correct because AWS DMS data validation is specifically designed to compare source and target data row-by-row after the initial load and during ongoing replication, ensuring data accuracy without requiring heavy queries on the source database - thus minimizing performance impact while confirming migration integrity before cutover.

Why the distractors are wrong:

• Option A (AWS SCT schema conversion) addresses schema/code conversion, not data validation - it's a necessary preparation step, not a validation mechanism for migrated data accuracy.
• Option B (DMS table metrics) only shows row counts and statistics like rows inserted/updated, which is insufficient for accurate data validation since it doesn't confirm the actual content of records matches between source and target.
• Option C (SCT premigration validation) focuses on assessing schema compatibility and conversion complexity before migration begins, not validating that data was accurately moved after migration.

Memory Tip: Think of it this way - SCT = Schema, DMS Metrics = Counts, DMS Data Validation = Content. When the question asks about data accuracy validation with minimal source impact, always look for DMS Data Validation, as it runs asynchronously in the background and performs the actual record-level comparison you need before cutover.

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