Which activity is specifically used to verify data quality against a trusted reference during CDX testing?

Which activity is specifically used to verify data quality against a trusted reference during CDX testing?

• A. Validate against source truth
• B. Define rules
• C. Run checks at stages
• D. Monitor quality metrics with automated tests

Answer: (A)

Verifying data quality against a trusted reference means using a known good dataset as the benchmark and comparing the data you have to that benchmark. In CDX testing, this is about aligning your data with the source of truth—the authoritative data you trust—to confirm accuracy, completeness, and consistency. By performing direct comparisons at the field and record level, you can spot discrepancies, such as missing values, mismatched IDs, or incorrect totals, and confirm that transformations or integrations haven’t introduced errors. This approach gives a concrete, objective check against the gold standard, which is exactly what “verify data quality against a trusted reference” is aiming for. The other activities matter for broader quality assurance: defining rules creates the criteria data should meet, running checks at stages ensures checks happen throughout the pipeline, and monitoring quality metrics with automated tests tracks ongoing performance. But none of these by themselves perform the explicit, benchmark-based validation against a trusted reference that confirms the data matches the source truth.

Verifying data quality against a trusted reference means using a known good dataset as the benchmark and comparing the data you have to that benchmark. In CDX testing, this is about aligning your data with the source of truth—the authoritative data you trust—to confirm accuracy, completeness, and consistency. By performing direct comparisons at the field and record level, you can spot discrepancies, such as missing values, mismatched IDs, or incorrect totals, and confirm that transformations or integrations haven’t introduced errors. This approach gives a concrete, objective check against the gold standard, which is exactly what “verify data quality against a trusted reference” is aiming for.

The other activities matter for broader quality assurance: defining rules creates the criteria data should meet, running checks at stages ensures checks happen throughout the pipeline, and monitoring quality metrics with automated tests tracks ongoing performance. But none of these by themselves perform the explicit, benchmark-based validation against a trusted reference that confirms the data matches the source truth.