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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - Enterprise AI risk program design - AI risk assessment and treatment strategies - AI governance communication and reporting - AI risk monitoring and continuous improvement |
| 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 Life Cycle Risk Management | - AI development, deployment, and monitoring risks - AI model and data risk identification - AI bias, drift, transparency, and control evaluation |
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質問 # 106
Which of the following BEST helps to ensure an AI system ' s potential human rights harms are managed?
正解：A
解説：
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.
質問 # 107
An organization uses multiple external data sources to train its AI models. Which of the following is the risk practitioner's BEST recommendation to protect the organization from data poisoning attacks?
正解：A
解説：
Data poisoning attacks involve malicious modification of training data to degrade model performance or introduce backdoors. With multiple external data sources, the attack surface for introducing poisoned data is broad and requires proactive, continuous detection at the ingestion stage.
Why B is Correct: The ISACA AAIR adversarial AI guidance identifies continuous monitoring and anomaly detection at the data ingestion pipeline as the most effective defense against data poisoning. By monitoring incoming data in real time for statistical anomalies, unexpected distributions, or known poisoning patterns, organizations can detect and block malicious data before it contaminates training datasets. This preventive approach is superior to reactive detection after poisoning has occurred.
Why A is Wrong: Reactive data integrity reviews triggered by model drift occur after poisoning has already affected model behavior. By this stage, the model may have been deployed and made harmful decisions.
Prevention during ingestion is superior to post-drift investigation.
Why C is Wrong: Model code and deployment artifact controls address security of the software pipeline but do not protect training data from external poisoning. Data integrity requires data-layer controls, not code security.
Why D is Wrong: Regularization reduces overfitting to training noise but does not detect or prevent deliberate poisoning attacks. A sufficiently targeted poisoning attack can introduce systematic bias that regularization techniques cannot mitigate.
質問 # 108
Which of the following is the MOST important objective when selecting AI controls based on dataset evaluation and validation?
正解：D
解説：
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. Dataset validation controls should protect the fairness and accuracy of model behavior by ensuring data is suitable, representative, and reliable. Real-time recalibration, framework alignment, and explainability monitoring are useful but do not replace this core objective. This makes option C, Preserving model fairness and 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 di rectly. 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.
質問 # 109
Which of the following is the MOST effective approach to help ensure compliance with AI acceptable use policies?
正解：B
解説：
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.
質問 # 110
Which of the following is the MOST appropriate key performance indicator (KPI) for the effectiveness of a targeted AI risk awareness training program?
正解：C
解説：
Training program effectiveness KPIs must measure behavioral change-whether training has actually altered how participants act in their work environment-rather than knowledge acquisition or adoption rates. The most meaningful behavioral signal for AI risk awareness training is whether trained users report suspicious activity.
Why B is Correct: According to ISACA AAIR training effectiveness measurement guidance, the number of AI irregularities and potential tampering incidents reported by users is the most appropriate behavioral KPI for risk awareness training effectiveness. When users report unusual AI behavior, this demonstrates they have internalized training concepts well enough to recognize anomalies and understand their obligation to report them. This behavioral change-from passive observation to active reporting-is the intended outcome of awareness training.
Why A is Wrong: Risk rating changes measure risk management process adjustments rather than individual behavioral change from training. Risk ratings reflect aggregate organizational risk, not the specific behavioral impact of an awareness training program.
Why C is Wrong: Ability to identify financial impacts is a knowledge assessment metric-it measures what users know rather than what they do differently as a result of training. Awareness training aims to change behavior, not just inform.
Why D is Wrong: AI adoption rates measure technology uptake, not risk awareness. Higher adoption could indicate confidence in AI systems but does not measure whether employees recognize and report AI risks- the specific objective of risk awareness training.
質問 # 111
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