What technique helps validate that the migrated dataset preserved cardinality?

• A. Row counts comparisons.
• B. UI screen validation.
• C. The color of data.
• D. Random sampling only.

Answer: (D)

Cardinality validation centers on ensuring the dataset size and structure remain consistent after migration. The most direct and reliable technique is row counts comparisons: tally how many rows exist in the source dataset and compare that total to the number of rows in the migrated target. If the totals match, you’ve confirmed that the migration preserved the overall volume and the basic one-to-one or one-to-many mappings that cardinality implies. For deeper assurance, you can extend this to per-group counts or checksums on key columns, but the essential idea is that matching totals strongly indicates cardinality was preserved.

Other options don’t directly verify the data’s size or relationships. UI screen validation checks what users see rather than the underlying record counts, and the color of data has no bearing on data integrity. Random sampling examines only a small portion of data and could miss discrepancies, so it isn’t sufficient on its own to prove cardinality preservation.