FAST Enterprises IC Interview Practice Test

The main idea is to validate a data migration with automated, comprehensive checks that compare the source and target datasets to ensure both completeness and accuracy of the data.

Automated checks that include row counts, sums, min/max, nulls, and key matches are the most effective because they provide a repeatable, end-to-end verification of the migrated data. Row counts confirm you moved the same number of records. Sums verify that numeric values haven’t changed during transfer. Min and max values help catch any data type issues, truncation, or out-of-range errors. Null checks ensure that required fields didn’t get dropped or misformatted. Key matches verify that identifiers and relationships are preserved, maintaining data integrity across related tables. Taken together, these checks detect a wide range of potential problems and can be run automatically across the entire dataset, giving confidence in the migration.

Manual checks of only a few rows miss the majority of issues in large datasets, relying on ETL logs only confirms that processes ran but not that the data values are correct, and skipping validation until go-live leaves you vulnerable to undetected data mismatches that can disrupt business operations.