We strongly recommend using our ISACA AAIR exam dumps to prepare for the ISACA AAIR certification. It is the best way to ensure success. With our ISACA Advanced in AI Risk (AAIR) practice questions, you can get the most out of your studying and maximize your chances of passing your ISACA Advanced in AI Risk (AAIR) exam.
| Section | Objectives |
|---|---|
| Topic 1: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 2: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 3: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 4: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 5: AI Risk Management | - Risk identification and assessment for AI systems
|
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NEW QUESTION # 39
Which of the following is the MOST important consideration when assessing risk for an AI system provided by a third party, as opposed to a non-AI related system?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Third-party AI requires greater continuous monitoring because models, data, dependencies, and provider behavior can change over time. Traditional onboarding assessments alone may not detect drift, updates, or newly introduced AI-specific risk. This makes option C, Greater emphasis on continuous monitoring due to the dynamically evolving nature of AI models, 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 # 40
An organization is designing an enterprise dashboard to support governance of its AI program. Which of the following is the risk practitioner's BEST recommendation?
Answer: A
Explanation:
An enterprise AI governance dashboard must provide decision-makers with a comprehensive, integrated view of AI program health across all dimensions-risk, performance, compliance, ethics, and operations.
Fragmenting this view or focusing on narrow metrics produces an incomplete governance picture.
Why B is Correct: The ISACA AAIR governance reporting guidance recommends aggregating diverse metrics from all AI life cycle stages as the best approach for an enterprise governance dashboard. This comprehensive aggregation enables decision-makers to see the full AI risk and performance picture-from data quality in training through deployment performance, bias monitoring, security incidents, and compliance status-in a single, actionable view. This unified perspective supports informed enterprise-level governance decisions.
Why A is Wrong: Uptime and availability metrics are operational infrastructure indicators that represent only one dimension of AI governance. Focusing primarily on availability misses critical governance concerns including model fairness, accuracy, bias, and ethical compliance.
Why C is Wrong: Risk heat maps based solely on training variance are narrow technical performance indicators. A governance dashboard requires breadth across risk types and life cycle stages, not depth on one specific technical metric.
Why D is Wrong: Assigning dashboard responsibility exclusively to IT centralizes governance reporting in one function that may lack visibility into business risk, ethical compliance, and strategic alignment dimensions of AI governance. Enterprise dashboards require cross-functional input and ownership.
NEW QUESTION # 41
Which of the following is the MOST critical component of a disaster recovery plan (DRP) for data, models, and AI infrastructure?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Disaster recovery for AI requires tested restoration processes for production-ready models and supporting artifacts, with version control and remote or segregated storage. Policies and insurance do not restore systems, while broader supply-chain issues belong to continuity planning. This makes option C, Restoration processes for production-ready AI models with version control and remote storage, 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 # 42
Which of the following is the GREATEST organizational risk when privacy, cybersecurity, and legal teams are not integrated into the AI oversight committee?
Answer: C
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. When privacy, cybersecurity, and legal functions are absent from integrated oversight, responsibilities can fall between organizational silos. The greatest risk is unclear accountability for control ownership, implementation, escalation, and remediation. This makes option D, Unclear accountability for AI control implementation, 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 # 43
Which of the following BEST supports the explainability and transparency of AI model deployments?
Answer: A
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.
Explainability depends on traceability. Documentation of model logic and data provenance provides evidence of how outputs were produced and which data influenced them, supporting auditability, accountability, and meaningful review of AI decisions. This makes option D, Documentation of model logic and data provenance, 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 pre serving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 44
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