What are data quality checks, and how are they enforced in Fabric pipelines?

• A. Data quality checks validate accuracy, completeness, and consistency; implemented as validation rules, tests, or data quality gates in pipelines with rollback on failure.
• B. They validate only schema and ignore data content.
• C. They can fix data automatically without any rules.
• D. They are optional and never rollback on failure.

Answer: (A)

Data quality checks are the rules and tests that verify data entering and moving through Fabric pipelines is accurate, complete, and consistent. In Fabric, you implement them as validation rules, tests, or data quality gates embedded in the pipeline. When data fails any check, the pipeline can stop and roll back the transaction or route the data to remediation, preventing bad data from advancing downstream. This protects downstream analytics by catching issues like missing values, out-of-range numbers, or inconsistent formats before they affect reports or models. Checking only the schema misses issues in the actual data content; automatic fixes without defined rules overlook what to correct; and quality checks in a well-governed pipeline are typically enforced with rollback on failure.