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| Section | Objectives |
|---|---|
| Topic 1: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Topic 2: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| Topic 3: AI Risk Management | - Risk identification and assessment for AI systems
|
| Topic 4: AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Topic 5: AI Lifecycle Controls | - Controls across AI development lifecycle
|
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NEW QUESTION # 129
Which of the following is the PRIMARY purpose of AI-driven classification controls such as image recognition or text sentiment analysis?
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.
Classification techniques are designed to assign inputs to defined categories, so their primary operational objective is reliable prediction or categorization. They do not inherently prevent poisoning or determine data quality, and classification is commonly supervised rather than unsupervised. This makes option C, Enhancing predictive accuracy, 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
Which of the following should be the PRIMARY consideration when determining the priority for restoration of AI systems following a model exfiltration attack?
Answer: C
Explanation:
Following a model exfiltration attack, multiple AI systems may require restoration. Prioritization must be based on objective criteria that reflect the potential business impact of continued unavailability. Systems supporting critical business functions must be restored before those supporting non-critical functions.
Why A is Correct: According to ISACA AAIR business continuity guidance for AI, the primary criterion for restoration priority is the AI system's criticality to business requirements. Systems that support mission- critical functions-patient care, financial transaction processing, safety operations-represent the highest restoration priority because their unavailability causes the greatest operational harm. This risk-based prioritization framework is consistent with standard business continuity management principles applied to the AI context.
Why B is Wrong: Team member expertise affects restoration capacity and speed but should not drive prioritization decisions. Priority is determined by business impact, not by where the team has the most technical capability. Resource allocation follows priority, not the reverse.
Why C is Wrong: Vulnerability testing and patch costs are operational considerations that may influence restoration timelines but should not override business criticality in determining priority. Cost-based prioritization could lead to restoring cheaper but less critical systems first.
Why D is Wrong: Dataset availability affects the feasibility and timeline of model retraining but is a logistical consideration rather than the primary basis for restoration priority. Critical systems should be prioritized even if their restoration is technically more complex.
NEW QUESTION # 131
Which of the following is the MOST important reason that risk practitioners should distinguish among traditional supervised models, unsupervised models, and large language models (LLMs) when assessing AI risk?
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. Supervised, unsupervised, and large language models have different risk profiles for explainability, fairness, robustness, hallucination, data dependence, and validation. Distinguishing model types allows the risk assessment to select controls that match the actual technology. This makes option A, Each type of model has different explainability, fairness, and resiliency profiles, 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 # 132
Which of the following metrics BEST indicates false positives in an AI model output?
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. 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 # 133
Which of the following BEST mitigates the risk of misaligned return on investment (ROI) in AI initiatives?
Answer: D
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 AI project outcomes to enterprise performance metrics ties investment to measurable organizational value. An inventory improves visibility and workforce metrics measure only one dimension, while a narrow focus on quantifiable benefits can miss strategic value. This makes option C, Mapping AI project outcomes to enterprise performance metrics, 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 # 134
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