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| Section | Objectives |
|---|---|
| Topic 1: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| Topic 2: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 4: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 174
An organization is launching multiple generative AI use cases. The risk practitioner needs to ensure that AI risk decisions align with business objectives and approved risk boundaries of the enterprise.
What is the best action to take?
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. AI risk should be explicitly included in enterprise risk appetite and tolerance so use cases are judged against approved business boundaries. A risk register and data controls are useful, but appetite and tolerance connect treatment decisions directly to business objectives. This makes option D, Include AI in risk appetite and 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 # 175
Which of the following is the MOST effective approach to help ensure compliance with AI acceptable use policies?
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. Acceptable use policies become effective when employees understand AI risks and how policy requirements apply in practice. Embedding AI risk awareness in training supports consistent user behavior, recognition of misuse, and appropriate escalation. This makes option D, Building trust by embedding AI risk awareness within organizational training, 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 # 176
Which of the following is the PRIMARY benefit of integrating AI-driven business intelligence into enterprise risk processes?
Answer: D
Explanation:
AI-driven business intelligence enhances the quality and timeliness of analytical outputs across enterprise risk processes. When integrated into risk management, AI analytics can continuously compare emerging risk signals against organizational risk thresholds, providing real-time alignment verification that human analysts cannot achieve at scale.
Why B is Correct: According to ISACA AAIR analytics integration guidance, the primary benefit of integrating AI-driven business intelligence into enterprise risk processes is enhanced alignment of analysis outputs with organizational risk thresholds. AI analytics tools continuously process risk data at scale, comparing patterns and emerging exposures against defined thresholds to provide risk practitioners with timely, calibrated intelligence. This alignment ensures risk responses are proportionate and that threshold breaches are detected promptly rather than discovered during periodic reviews.
Why A is Wrong: Cost reduction through eliminating redundant oversight mechanisms is an efficiency benefit that may result from process optimization but is not the primary purpose of integrating AI-driven business intelligence. Oversight mechanisms may be streamlined but rarely eliminated entirely.
Why C is Wrong: Automated production of technical performance reports is an operational reporting automation benefit. While valuable for reducing manual reporting burden, it is a narrow operational benefit compared to the strategic risk alignment value of AI-enhanced analytics.
Why D is Wrong: AI inventory governance and classification is a risk management administration function.
Centralizing these activities is an organizational efficiency benefit, not the primary value delivered by AI- driven business intelligence integration into risk processes.
NEW QUESTION # 177
Which of the following is the MOST significant risk when an organization depends on a single AI vendor ' s proprietary environment?
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. Dependence on a single proprietary AI environment can create vendor lock-in, making migration technically difficult, operationally disruptive, and expensive. Transparency and scalability are relevant concerns, but prohibitive switching costs are the central dependency risk. This makes option B, Prohibitive migration costs, 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 # 178
Which of the following metrics BEST indicates false positives in an AI model output?
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. Precision is directly affected by false positives because it measures the proportion of predicted positives that are actually positive. Recall is more sensitive to false negatives, F1 combines both dimensions, and overall accuracy can hide a high false-positive rate. This makes option A, Precision, 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 # 179
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