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ISACA Advanced in AI Risk Sample Questions:

1. Which of the following is the MOST important consideration when determining mitigation controls for an AI system?

A) Determining control performance baselines and reporting requirements for regulatory compliance
B) Evaluating control effectiveness and costs against potential business losses from unmitigated AI risk
C) Providing comprehensive AI risk awareness training to security and technical personnel
D) Prioritizing controls based on the complexity and computational requirements of the AI system

2. A risk practitioner is reviewing an organization's implementation of a business-critical AI decision system.
Which of the following would be of GREATEST concern?

A) Risk threshold acceptance criteria that do not require 100% decision accuracy
B) Lack of cross-functional AI incident identification and escalation training
C) Reliance on conventional third-party security providers for system monitoring
D) Insufficient scenario-based testing of system failure modes and recovery procedures

3. Which of the following is a risk practitioner's BEST recommendation to establish accountability for AI system outputs and decisions?

A) Centralized governance task force for model decision authority
B) Formal documented role assignments with named owners
C) Continuous monitoring and key performance indicators (KPIs)
D) Regular reviews of resource allocation for AI projects

4. An organization has deployed an AI system that initially performs well but whose outputs deteriorate over time despite stable input characteristics. Which of the following is the BEST course of action?

A) Focus efforts on dataset cleansing and documentation prior to further system updates.
B) Establish continuous performance monitoring and scheduled system recalibration.
C) Engage periodic external audits of model source code and implement peer code reviews.
D) Replace the system's predictive capability with static rule-based controls and fixed decision logic.

5. Which of the following is the PRIMARY benefit of implementing a comprehensive data pipeline for AI model training, testing, and validation?

A) Enhanced auditability of outputs to provide evidence of regulatory compliance
B) Reduced risk of introducing errors into the final AI model
C) Sharing of governance risk with external data and service providers
D) Automation of complex tasks in early stages of the data pipeline

Solutions: