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| Section | Objectives |
|---|---|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
>> AAIR Exam Questions Answers <<
PassTestking offers the complete package that includes all exam questions conforming to the syllabus for passing the ISACA Advanced in AI Risk (AAIR) exam certificate in the first try. These formats of actual ISACA AAIR Questions are specifically designed to make preparation easier for you.
NEW QUESTION # 137
Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?
Answer: C
Explanation:
Enterprise risk framework integration elevates AI risk management from a technical discipline to a strategic organizational function, ensuring AI risks are considered alongside all other enterprise risks in strategic planning and decision-making.
Why D is Correct: The ISACA AAIR curriculum identifies enterprise integration as the mechanism that enables organization-level oversight and ensures AI risk management aligns with strategic objectives, risk appetite, and governance structures. This integration allows the board and senior management to make informed decisions about AI investment, deployment, and risk acceptance with full awareness of AI's contribution to the organizational risk profile.
Why A is Wrong: KPI benchmarking is an operational performance management activity. While integration may improve KPI accuracy, this is a secondary operational benefit rather than the primary strategic benefit of ERM integration.
Why B is Wrong: Regulatory compliance is improved by integration but represents a specific compliance benefit rather than the primary organizational value. Compliance is an output of good governance, not the purpose of ERM integration.
Why C is Wrong: Cyber threat identification is a security function that benefits from integration but is not the primary benefit. Many AI risks are non-cyber in nature-fairness, accuracy, transparency-and would not be captured by a cyber-focused framing.
NEW QUESTION # 138
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: D
Explanation:
Model drift occurs when the statistical relationship between model inputs and outputs changes over time, causing previously accurate predictions to become less reliable. Regular retraining with updated, relevant data recalibrates the model to current real-world patterns.
Why A is Correct: According to ISACA AAIR model maintenance guidance, regular retraining with new relevant datasets is the most direct mitigation for model drift. By periodically retraining on current data, the model learns the latest patterns and relationships-counteracting the drift that accumulates as real-world conditions diverge from the original training data. This is the standard industry practice for maintaining production AI models in dynamic environments.
Why B is Wrong: Restricting automated data validation to low-risk models creates a governance double standard that leaves high-risk models more vulnerable. If anything, high-risk models require more rigorous automated validation, not less. This approach increases rather than mitigates drift risk for critical applications.
Why C is Wrong: Maintaining existing dataset variance during preprocessing preserves statistical characteristics from a historical snapshot. If drift has occurred in real-world data, deliberately maintaining old variance levels prevents the model from adapting to new conditions.
Why D is Wrong: Role-based access controls protect model parameters and data from unauthorized modification. While important for security, access controls do not address model drift, which is driven by changing real-world conditions rather than unauthorized changes.
NEW QUESTION # 139
An organization plans to license an AI model trained on proprietary data to external partners. Which risk should the risk practitioner prioritize?
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. Licensing a model trained on proprietary information creates a material risk that confidential intellectual property may be memorized or surfaced in generated outputs. Encryption during training does not eliminate output leakage, and transparency concerns are secondary to direct IP exposure. This makes option D, Exposure of confidential intellectual property in generated content, 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 # 140
A risk practitioner learns that a credit-scoring AI system is exhibiting bias that cannot be eliminated through further training. Which of the following is the risk practitioner ' s BEST recommendation?
Answer: B
NEW QUESTION # 141
Which of the following is the PRIMARY purpose of applying thorough cleansing and normalization to AI training data?
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. Cleansing and normalization reduce inconsistent, erroneous, or statistically distorted training data that can produce biased or inaccurate model behavior. Their primary value is improving trustworthy inputs rather than lowering compute cost or satisfying privacy law by themselves. This makes option C, Addressing statistical distortions in training data that could cause model bias, 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 # 142
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