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| Section | Objectives |
|---|---|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
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NEW QUESTION # 99
Which of the following is the BEST course of action to mitigate risk during model selection of supervised or unsupervised algorithms?
Answer: B
Explanation:
Algorithm selection is a foundational risk management decision in AI development. The wrong algorithm for a given use case can produce inaccurate, unreliable, or harmful outputs regardless of the quality of training data or computational resources applied.
Why D is Correct: The ISACA AAIR model development guidance identifies use case alignment as the most critical algorithm selection criterion. Supervised and unsupervised learning are suited to fundamentally different problem types-supervised learning requires labeled training data and learns mappings to known outputs; unsupervised learning discovers patterns in unlabeled data. Selecting algorithms whose capabilities match the use case's structure and objectives prevents systematic performance failures and misapplied AI.
Why A is Wrong: Generalization capability is an important model quality criterion but represents one of many algorithmic properties. Strong generalization on the wrong problem type still produces poor results. Use case alignment precedes generalization as a selection criterion.
Why B is Wrong: Requiring supervised learning for all training projects is an inappropriate blanket policy.
Many valuable use cases-anomaly detection, customer segmentation, exploratory analytics-are better served by unsupervised approaches. Mandating supervised learning prevents optimal use case matching.
Why C is Wrong: Computational cost is a resource management consideration. Optimizing for cost at the expense of use case fit risks deploying inappropriate models that produce unreliable outputs, creating far greater costs through remediation or harm.
NEW QUESTION # 100
An AI risk assessment identifies high-impact exploitable vulnerabilities in an AI model. No practical remediation controls are available to address these flaws before the planned go-live date.
What is the risk practitioner’s recommended risk response?
Answer: B
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 high-risk model with known high-impact exploitable vulnerabilities should not be released when no effective fixes are available. Postponing launch is a risk-avoidance response that prevents knowingly introducing unacceptable exposure into production. This makes option D, Postpone the launch and communicate the delay to stakeholders, 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 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 # 102
Which of the following is the MOST effective approach to help ensure compliance with AI acceptable use policies?
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. 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 # 103
A risk practitioner is developing new risk scenarios related to an organization ' s use of AI tools to predict industrial equipment failures. Which of the following is MOST important for the risk practitioner to verify?
Answer: B
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. Predictive-maintenance models can become unreliable when equipment behavior, operating conditions, or sensor relationships change. The practitioner should verify that the model can adapt to changing operational conditions rather than merely checking software version or end-user training effort. This makes option C, The model is capable of adjusting quickly to changes in operational conditions, 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 # 104
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