Which statement best describes how MLOps supports AI governance?

• A. It focuses on maximizing model complexity without governance considerations.
• B. It provides standardized processes for versioning, testing, deployment, monitoring, and governance across the model lifecycle.
• C. It centralizes procurement decisions for AI vendors.
• D. It ensures privacy by design across all data handling stages.

Answer: (B)

MLOps provides standardized, repeatable pipelines across the model lifecycle to enable governance. By defining version control for models and data, automated testing and validation, controlled deployment, continuous monitoring for performance and drift, and integrated governance checks, organizations can audit decisions, reproduce results, enforce policies, and manage risk. This alignment ensures that every model change is traceable, testable, and compliant, which is essential for AI governance. The other options miss the governance spine: pursuing greater model complexity without governance neglects the need for policy and control; centralizing procurement decisions sits outside the lifecycle processes that MLOps standardizes; and privacy by design focuses on data handling rather than the end-to-end lifecycle orchestration and governance controls embedded in MLOps.