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ISACA AAIR practice questions are based on recently released ISACA AAIR exam objectives. Includes a user-friendly interface allowing you to take the ISACA Advanced in AI Risk practice exam on your computers, like downloading the PDF, Web-Based AAIR Practice Test PracticeMaterial, and Desktop ISACA AAIR practice exam PracticeMaterial.
| Section | Objectives |
|---|---|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 153
Which of the following is the MOST effective way to gain insight into an organization ' s exposure to AI- powered threats?
Answer: D
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. Continuous monitoring and behavioral analytics provide the broadest insight into AI-powered threats by detecting anomalies and evolving patterns that may not match known signatures. Static diagrams and signature-only detection are less effective against adaptive attacks. This makes option B, Implement continuous monitoring and behavior analytics, 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 # 154
An organization formally accepts residual risk for a low-impact AI capability after confirming it falls within the enterprise risk tolerance.
What is the primary benefit of this risk acceptance action?
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. When residual risk is within tolerance and affects non-critical functions, formal acceptance is proportionate and allows scarce mitigation resources to be directed toward higher-impact exposures.
Acceptance must still be documented and monitored rather than treated as inaction. This makes option A, It enables the allocation of mitigation resources to more critical AI-specific issues, 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 # 155
Which of the following should be a risk practitioner's PRIMARY consideration when developing risk scenarios related to adversarial manipulation of a business-critical AI system?
Answer: D
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. Risk scenarios for adversarial manipulation should be classified using criteria aligned with organizational tolerance so likelihood and impact can drive proportionate treatment decisions. Attack prevalence and patch frequency are inputs, not the governing risk criterion. This makes option C, Alignment of risk classification criteria with organizational tolerance, 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 # 156
Which of the following is the GREATEST risk when AI governance fails to incorporate stakeholder perspectives in policy and design decisions?
Answer: B
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. Excluding stakeholder perspectives can produce AI behavior and outputs that do not match the intended business case, affected-user needs, or acceptable values. Governance must incorporate those perspectives so technical success does not become business or societal misalignment. This makes option A, Misalignment of AI behavior and outputs with expected business cases, 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 # 157
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
Answer: C
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.
Representative sampling combined with explicit bias mitigation addresses both the root cause and manifestation of fairness risk. Label cleanup and synthetic data can help, but neither guarantees representative coverage or equitable outcomes by itself. This makes option D, Representative sampling strategies combined with bias mitigation controls, 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 # 158
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