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| Section | Objectives |
|---|---|
| Topic 1: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 2: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 4: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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NEW QUESTION # 108
Which of the following would BEST mitigate operational and legal risk exposure when integrating third-party AI services?
Answer: A
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. Third-party AI creates shared operational and legal obligations, so a responsibility matrix should clearly define accountability for controls, data, incidents, compliance, and remediation. Delegation does not remove the organization ' s own responsibility. This makes option C, Defining a responsibility matrix and accountability for all parties, 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 # 109
Which of the following BEST mitigates risk associated with evasion attacks on AI models?
Answer: D
Explanation:
Evasion attacks involve adversaries crafting inputs specifically designed to fool AI models into producing incorrect outputs-for example, manipulating images to evade object detection or modifying text to bypass content classifiers. Detecting these attacks requires identifying inputs that are statistically unusual or inconsistent with legitimate use patterns.
Why B is Correct: The ISACA AAIR adversarial AI security guidance identifies anomaly detection as the most effective mitigation for evasion attacks. Anomaly detection systems monitor input distributions, model query patterns, and output characteristics for statistical deviations that indicate adversarial manipulation. By identifying inputs that fall outside expected distributions or trigger unusual model responses, anomaly detection catches evasion attempts before they produce harmful outputs.
Why A is Wrong: API rate limiting controls query frequency to prevent brute-force model probing but does not detect or prevent crafted adversarial inputs sent at normal rates. An attacker can evade rate limits by spacing requests or distributing queries.
Why C is Wrong: Predictive analytics uses historical patterns to forecast future outcomes. It does not specifically detect real-time adversarial manipulation of model inputs.
Why D is Wrong: Feature importance weighting adjusts how much different input features influence model predictions. While it can improve robustness to irrelevant features, it does not detect adversarial inputs specifically crafted to exploit important features.
NEW QUESTION # 110
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: D
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 # 111
Which of the following is MOST important to evaluate when selecting a vendor for a third-party large language model (LLM)?
Answer: D
Explanation:
Third-party LLMs process organizational data-including sensitive and proprietary information-during both training and inference. The vendor's data handling practices determine whether the organization's data remains private, secure, and compliant with legal obligations.
Why D is Correct: According to ISACA AAIR third-party risk guidance, data handling practices are the most critical evaluation criterion for AI vendors. How the vendor uses input data-whether for model training, analytics, or retention-directly determines data privacy risk, intellectual property exposure, and regulatory compliance. Vendors who train on customer input data without restriction create significant privacy and confidentiality risks.
Why A is Wrong: SLA alignment with corporate strategy addresses availability and performance obligations.
While important, these commercial terms do not address the fundamental data risk created by vendor data handling practices.
Why B is Wrong: ML method selection reflects technical sophistication but does not determine data risk. The risk profile is driven by data governance, not algorithmic choice.
Why C is Wrong: Subscription models represent commercial and procurement considerations. Pricing structure has no bearing on data privacy risk or the organization's risk exposure from vendor data practices.
NEW QUESTION # 112
Which of the following BEST helps to ensure an AI system ' s potential human rights harms are managed?
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. Potential human-rights harms are best managed through recurring stakeholder engagement in risk treatment because affected people may reveal impacts that technical metrics or internal policy mapping miss. Ongoing participation supports identification, escalation, and remediation of evolving harms. This makes option C, Engaging stakeholders in recurring risk treatment processes, 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 # 113
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