20+ practice questions focused on AI Risk Program Management — one of the most tested topics on the ISACA Advanced in AI Risk (AAIR) (AAIR) exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start AI Risk Program Management PracticeWhich THREE metrics should be tracked to assess the 'Risk' of an AI model in production?
Explanation: Model accuracy/error rate (A) is a core risk metric because it directly quantifies how often the model produces incorrect outputs, which drives business, safety, and compliance exposure in production. Model latency (D) matters for risk because slow response times can violate SLAs, degrade user experience, and cause downstream failures in real-time or automated decision systems. Data drift (E) is essential because shifts between production input distributions and training data can silently degrade model reliability and validity, increasing the likelihood of harmful or non-compliant predictions. The unmarked options do not belong: the brand of the coffee machine (B) and the number of employees in the break room (C) are irrelevant workplace trivia with no bearing on AI model risk.
Which TWO actions are required to manage 'Third-Party' AI risks?
Explanation: Option A is correct because managing third-party AI risk begins with due diligence: you must assess the vendor's AI governance, model training data provenance, bias testing, security controls, and compliance with frameworks such as ISO/IEC 42001 or the NIST AI RMF before onboarding or renewing the relationship. Option E is correct because a signed NDA is a contractual control that legally binds the vendor to confidentiality, protecting proprietary data, model inputs, and any sensitive information shared during the AI engagement. The remaining options do not belong: ignoring vendor feedback (B) removes a key input for continuous risk monitoring, letting the vendor set their own rules (C) abandons organizational oversight and accountability, and asking for a cafeteria menu (D) is irrelevant to AI risk management.
Which TWO teams should define the 'Risk Appetite' for a new AI project?
Explanation: Option A (Senior Leadership/Business Owners) is correct because risk appetite is a strategic decision about how much uncertainty and potential loss the organization is willing to accept in pursuit of the AI project's objectives, and only those accountable for business outcomes have the authority to set that threshold. Option B (The Risk Management/Compliance Office) is correct because this function translates leadership's tolerance into measurable risk limits, ensures alignment with regulatory and ethical obligations, and provides the governance framework for monitoring AI risks. Options C, D, and E are incorrect because a weather station, landscaping team, and company mascot have no governance authority, fiduciary responsibility, or risk oversight role in defining an AI project's risk appetite.
Your organization is experiencing 'AI Shadow IT'. What management action is most effective for bringing these initiatives into the Risk Program?
Explanation: Visibility is the foundation of risk management. Implementing a centralized intake process forces shadow initiatives to be assessed.
When setting KPIs for an AI risk program, which metric is a leading indicator of potential model bias?
Explanation: Demographic parity ratio in training data samples is a leading indicator because it measures the representation of protected groups before the model is deployed. A skewed ratio (e.g., far from 1.0) signals that the model may learn biased patterns, allowing proactive mitigation. Unlike lagging metrics such as complaints, it provides early warning during data collection and preprocessing.
+15 more AI Risk Program Management questions available
Practice all AI Risk Program Management questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of AI Risk Program Management. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
AI Risk Program Management questions on the AAIR frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. AI Risk Program Management is tested as part of the ISACA Advanced in AI Risk (AAIR) (AAIR) blueprint. Practicing with targeted AI Risk Program Management questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but AI Risk Program Management is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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