20+ practice questions focused on AI Governance And Risk — one of the most tested topics on the ISACA Advanced in AI Audit (AAIA) (AAIA) exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start AI Governance And Risk PracticeYou are auditing a firm's AI policy and find that the 'Model Inventory' in the enterprise risk management platform lacks versioning metadata. Which specific control gap should be prioritized to align with NIST AI RMF?
Explanation: NIST AI RMF's Govern and Map functions require maintaining a model inventory with sufficient metadata to trace model lineage, changes, and approvals. Missing versioning metadata means there is no immutable record of architecture changes, so the prioritized control gap is the lack of immutable audit logs for model architecture changes, which directly undermines traceability and accountability.
During an audit of AI procurement, you find that the 'Vendor AI Assessment' questionnaire is missing. What is the correct next step?
Explanation: An auditor must ensure that third-party AI risks are evaluated using a formal, documented process.
During an audit of Google Cloud Vertex AI, you discover that sensitive PII is being logged during model inference. You need to implement a control that enforces privacy at the model serving layer. Which configuration should you verify?
Explanation: Vertex AI Model privacy and data masking controls are the specific serving-layer feature that lets you redact or mask sensitive fields (such as PII) before they are written to prediction logs. Verifying this configuration ensures that inference requests/responses captured by Vertex AI logging pipelines are sanitized at the model serving layer, satisfying the audit requirement.
An organization is using 'Federated Learning' to maintain data privacy while training models. What is the primary audit risk associated with this architecture?
Explanation: In Federated Learning, local model updates (gradients) can potentially reveal information about the underlying training data, necessitating 'Differential Privacy' as a control.
Which TWO of the following are legitimate 'AI Governance' concerns regarding third-party AI service providers (SaaS)?
Explanation: Option B is correct because AI governance for third-party SaaS providers must address where training data, prompts, and outputs are stored and processed, since data residency directly affects compliance with regulations such as GDPR, HIPAA, or sector-specific data localization laws. Option C is correct because governance requires contractual third-party audit rights and transparency into how the vendor develops, trains, and validates its models, enabling accountability for bias, safety, and provenance. Option A is not a governance concern because the vendor's programming language is an implementation detail that does not affect compliance, risk, or accountability. Option D is not selected because, while model update and retraining frequency can be a technical or operational consideration, it is not one of the two governance concerns identified here. Option E is not selected because the physical location of a corporate office is distinct from data residency and does not by itself determine regulatory compliance.
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Practice all AI Governance And Risk questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of AI Governance And Risk. 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 Governance And Risk questions on the AAIA 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 Governance And Risk is tested as part of the ISACA Advanced in AI Audit (AAIA) (AAIA) blueprint. Practicing with targeted AI Governance And Risk questions ensures you can handle any format or difficulty that appears.
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