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ISACA AAIR Exam Syllabus Topics:

SectionWeightObjectives
AI Life Cycle Risk Management- AI bias, drift, transparency, and control evaluation
- AI model and data risk identification
- AI development, deployment, and monitoring risks
AI Risk Governance and Framework Integration37%- AI Organizational Processes and Alignment
- AI Models, Frameworks, Strategies, and Use Cases
- AI Ownership, Oversight, and Accountability
AI Risk Program Management42%- AI governance communication and reporting
- AI risk assessment and treatment strategies
- AI risk monitoring and continuous improvement
- Enterprise AI risk program design

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ISACA Advanced in AI Risk Sample Questions (Q27-Q32):

NEW QUESTION # 27
A risk practitioner assesses a new AI system and determines that the risk is within the organization ' s risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?

Answer: A


NEW QUESTION # 28
An organization is integrating AI systems into core business operations and has decided to establish a formal process to align AI initiatives with corporate values. Which of the following is the GREATEST benefit of this decision?

Answer: D


NEW QUESTION # 29
Which of the following is the GREATEST organizational risk when AI performance alerts are not escalated to decision-makers for review and decisioning?

Answer: A


NEW QUESTION # 30
Which of the following is the PRIMARY benefit of implementing a formal data disposition process for AI training and operational datasets?

Answer: D

Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. A formal data disposition process reduces unnecessary retention of AI training and operational data, thereby lowering the amount of sensitive information exposed to breach and reducing noncompliance with retention or data- minimization obligations. This makes option D, Reducing exposure to breaches and regulatory penalties, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.


NEW QUESTION # 31
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?

Answer: B

Explanation:
Third-party LLMs process organizational data-including sensitive and proprietary information-during both training and inference. The vendor's data handling practices determine whether the organization's data remains private, secure, and compliant with legal obligations.
Why D is Correct: According to ISACA AAIR third-party risk guidance, data handling practices are the most critical evaluation criterion for AI vendors. How the vendor uses input data-whether for model training, analytics, or retention-directly determines data privacy risk, intellectual property exposure, and regulatory compliance. Vendors who train on customer input data without restriction create significant privacy and confidentiality risks.
Why A is Wrong: SLA alignment with corporate strategy addresses availability and performance obligations.
While important, these commercial terms do not address the fundamental data risk created by vendor data handling practices.
Why B is Wrong: ML method selection reflects technical sophistication but does not determine data risk. The risk profile is driven by data governance, not algorithmic choice.
Why C is Wrong: Subscription models represent commercial and procurement considerations. Pricing structure has no bearing on data privacy risk or the organization's risk exposure from vendor data practices.


NEW QUESTION # 32
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