Question 35
Domain 2 — AI Operations, Lifecycle, and Control EnvironmentData lineage auditing is MOST concerned with:
Correct answer: C
Explanation
Data lineage auditing focuses on tracking where data comes from, how it changes, and where it goes. It is about tracing the "origin, transformation, and movement of data through the AI pipeline," which supports accountability, reproducibility, and impact analysis.
Why each option is right or wrong
A. Tracking model performance metrics over time after deployment
B. Verifying encryption standards applied to training datasets at rest
C. Tracing the origin, transformation, and movement of data through the AI pipeline
Data lineage auditing examines the end-to-end provenance of data artifacts in an AI system, including source, intermediate transformations, and downstream propagation through ingestion, preprocessing, training, and deployment. In practice, this is the audit trail needed to reconstruct how a dataset or feature set was derived and to identify where a specific record was altered or used; without that chain, accountability and reproducibility cannot be established.
D. Auditing third-party AI vendor contractual compliance