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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI bias, drift, transparency, and control evaluation - AI model and data risk identification | |
| Topic 2: AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI governance communication and reporting - AI risk monitoring and continuous improvement |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
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NEW QUESTION # 100
Which of the following is the GREATEST risk when an AI model is trained on multiple data sources that differ in update frequency and quality?
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. Training from sources with different update frequencies and quality can introduce conflicting, stale, or incomplete information, leading to inconsistent model outputs and operational decisions. Cost and integration issues are secondary to unreliable decision quality. This makes option A, Inconsistency in model outputs and operational decisions, 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 # 101
Which of the following is the PRIMARY risk associated with the use of unsupervised learning methods to train AI models?
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.
Unsupervised learning operates without predefined labels, making discovered patterns harder to validate and explain against known targets. This creates particular assurance challenges around interpretation, labeling, and explainability compared with supervised classification. This makes option D, Lack of labeling and explainability, 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 # 102
Which of the following is the MOST effective way to ensure identified AI risk is considered in business strategy 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. Mapping identified AI risks to organizational objectives translates technical exposure into business consequences and ensures risk is considered in strategic decisions. Workshops and vulnerability reports help identify issues but do not create the same direct strategy linkage. This makes option A, Map AI risk to organizational objectives, 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 # 103
Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?
Answer: A
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 # 104
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: C
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 # 105
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