Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.
Option A focuses on retraining during outages (a niche scenario) rather than validating service continuity for production inference. Option B is security testing, not BC/DR validation. Option C tests data loss handling but does not comprehensively validate AI service behavior across failover and recovery.
[References: AI Security Management™ (AAISM) Body of Knowledge: “Operational Resilience—BC/DR for AI Systems,” “Validation and Evidence of Continuity”; AAISM Study Guide: “AI DR Test Planning—Metrics, Model Performance Validation, and Recovery Readiness.”, ===========, ]
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