Which of the following BEST ensures AI components are validated as part of disaster recovery testing?

The correct answer is D. Monitoring model performance metrics during failover and recovery to assess system stability. Disaster recovery (DR) testing validates that systems behave correctly and maintain acceptable performance after a failure event. Monitoring model performance metrics - such as inference accuracy, latency, and prediction consistency - during failover directly verifies that AI…

AI Security Assurance and Resilience

Question

Options

ADisconnecting primary model training clusters to test retraining workflow during extended outages
BSimulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
CRunning simulated data loss scenarios by erasing test records from the AI system's feature store
DMonitoring model performance metrics during failover and recovery to assess system stability

How the community answered

(57 responses)

A
5% (3)
B
4% (2)
C
16% (9)
D
75% (43)

Explanation

Disaster recovery (DR) testing validates that systems behave correctly and maintain acceptable performance after a failure event. Monitoring model performance metrics - such as inference accuracy, latency, and prediction consistency - during failover directly verifies that AI components recover correctly and continue operating within acceptable bounds. Option A (disconnecting training clusters) tests a specific training workflow but not the full AI system recovery. Option B (simulating DoS attacks) is a security/resilience test, not DR validation. Option C (erasing test records) could cause irreversible damage and is not a standard DR test procedure.

Topics

#Disaster Recovery Testing#AI System Resilience#Model Performance Monitoring#Failover Validation

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