Which items are typically checked by a data quality rule in Foundry Aware?

• A. All of the above
• B. Nulls
• C. Ranges
• D. Uniqueness

Answer: (A)

All of the above is the best choice because data quality rules in Foundry Aware are designed to validate multiple aspects of data, not just one. Checking for nulls catches missing values that can derail analyses. Range checks ensure values stay within expected boundaries, catching outliers or misentries. Uniqueness checks prevent duplicates that can distort calculations and data integrity. A solid data quality approach typically includes all these checks, either together or as separate rules, so covering nulls, ranges, and uniqueness provides the most robust protection against bad data.