Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. Rigorous testing and validation before production primarily reduce the likelihood of in accurate, biased, unsafe, or otherwise unreliable outputs reaching real users. Compliance readiness may improve, but validation is fundamentally about demonstrating acceptable model behavior before deployment. This makes option B, Minimizing risk related to inaccurate or biased decision outputs, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.