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| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI model and data risk identification - AI bias, drift, transparency, and control evaluation | |
| AI Risk Governance and Framework Integration | 37% | - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| AI Risk Program Management | 42% | - AI governance communication and reporting - AI risk monitoring and continuous improvement - Enterprise AI risk program design - AI risk assessment and treatment strategies |
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NEW QUESTION # 126
An organization has deployed an AI system that initially performs well but whose outputs deteriorate over time despite stable input characteristics. Which of the following is the BEST course of action?
Answer: A
Explanation:
Output deterioration despite stable inputs is a classic indicator of model drift-specifically concept drift, where the underlying relationships between inputs and targets change over time even when the distribution of inputs appears stable. This requires ongoing monitoring and systematic recalibration.
Why D is Correct: The ISACA AAIR life cycle management guidance identifies continuous performance monitoring and scheduled recalibration as the appropriate response to model drift. Monitoring provides early warning when performance degrades below thresholds, while scheduled recalibration ensures the model is periodically updated to reflect current real-world patterns. This systematic approach prevents continued deterioration and maintains model reliability.
Why A is Wrong: Source code audits and peer reviews address development quality and code integrity, not model drift. Drift is a statistical phenomenon driven by changing data relationships, not code defects that code reviews can identify.
Why B is Wrong: Replacing predictive AI with static rule-based systems eliminates the adaptive capabilities that make AI valuable. Static rules cannot respond to evolving patterns and typically perform worse in dynamic environments.
Why C is Wrong: Dataset cleansing addresses data quality for model retraining but does not establish the ongoing monitoring mechanism needed to detect future drift. A one-time cleansing activity cannot prevent recurrent deterioration.
NEW QUESTION # 127
Which of the following is the MOST suitable key risk indicator (KRI) for model drift resulting in biased or discriminatory outputs?
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. A disparate impact ratio directly measures differences in favorable outcomes across groups and is therefore the most relevant KRI when the stated concern is discriminatory output. Population stability measures distributional drift but does not directly establish disparate treatment. This makes option C, Disparate impact ratio, 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 # 128
Which of the following is the PRIMARY benefit of aligning AI risk management with existing organizational governance frameworks?
Answer: A
Explanation:
Organizational governance frameworks provide the structures, processes, and oversight mechanisms through which enterprises manage their activities and risks. Aligning AI risk management with these frameworks ensures AI activities receive the same level of strategic oversight as other organizational functions.
Why C is Correct: The ISACA AAIR curriculum identifies enterprise-level oversight and strategic alignment as the primary benefit of governance framework integration. When AI risk management operates within established governance structures, AI decisions are subject to the same approval authorities, risk escalation pathways, and strategic alignment checks that govern all major organizational decisions. This produces coherent, enterprise-aware AI governance.
Why A is Wrong: Role development and responsibility clarification are governance activities that may result from alignment, but they represent structural outputs rather than the primary benefit. The benefit is the oversight quality, not the organizational structure itself.
Why B is Wrong: Expediting compliance approvals is an efficiency benefit that may arise from better- organized governance. However, speed of approval is not the primary purpose of framework alignment-the purpose is quality and consistency of oversight.
Why D is Wrong: Standardizing acquisition processes is a procurement function benefit. While governance alignment may improve procurement consistency, standardization is a narrow operational benefit compared to the strategic oversight value of full governance integration.
NEW QUESTION # 129
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 # 130
A risk practitioner discovers that an AI chatbot stores user interactions indefinitely. Which of the following is the MOST appropriate mitigation?
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. Indefinite chatbot retention creates unnecessary privacy and breach exposure. Automated retention limits linked to documented consent or purpose ensure records are kept only as long as justified while still accommodating legitimate legal, operational, or audit requirements. This makes option B, Automate retention limits linked to documented consent, 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 # 131
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