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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Identification and Assessment |
| AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation - AI Data and Asset Management |
| AI Risk Governance and Framework Integration | 37% | - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations - AI Organizational Processes and Alignment |
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質問 # 181
Which of the following BEST mitigates privacy exposure when using datasets for AI model training that include personal information?
正解：D
解説：
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. De- identification directly reduces privacy exposure by removing or transforming identifying information before personal data is used for training. Authentication and human review protect access or oversight but do not remove personal information from the dataset itself. This makes option C, Implementing robust de- identification controls, 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 # 182
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
正解：B
解説：
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.
Representative sampling combined with explicit bias mitigation addresses both the root cause and manifestation of fairness risk. Label cleanup and synthetic data can help, but neither guarantees representative coverage or equitable outcomes by itself. This makes option D, Representative sampling strategies combined with bias mitigation controls, 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 # 183
After which of the following events is it MOST important to update risk ratings?
正解：C
解説：
Risk ratings must be maintained as current assessments of organizational risk exposure. Events that materially change the risk profile-particularly those indicating active harm or regulatory violations-require immediate risk rating updates to ensure governance responses are calibrated to the current risk reality.
Why A is Correct: According to ISACA AAIR risk monitoring and review guidance, the discovery of discriminatory outputs from an AI system represents a material change in risk exposure that requires immediate risk rating updates. Discriminatory outputs indicate active harm to individuals, regulatory violations, and significant legal and reputational exposure. This event fundamentally changes the risk profile from a potential to an actual harm, requiring escalated risk ratings and treatment responses.
Why B is Wrong: Adding new monitoring metrics improves risk detection capability but does not change the underlying risk levels. New metrics may subsequently detect risks requiring rating updates, but their addition alone is an operational change, not a risk level change.
Why C is Wrong: Vulnerability patch deployment reduces risk by closing specific security gaps, which may lower risk ratings but is less urgent than updating ratings to reflect active harm discovery. Patching is a remediation activity; discriminatory outputs represent ongoing harm requiring immediate escalation.
Why D is Wrong: Creating an oversight committee improves governance capability but does not change the risk profile of AI systems. Governance structure changes affect the organization's ability to manage risk; they do not affect the risk levels themselves.
質問 # 184
Which of the following is the MOST important benefit of deploying continuous monitoring and automated anomaly detection for AI models in production?
正解：D
解説：
Production AI models face ongoing threats from adversarial attacks, unauthorized modifications, and parameter tampering. Continuous monitoring and automated anomaly detection provide real-time visibility into model behavior deviations that indicate security incidents or unauthorized changes.
Why C is Correct: The ISACA AAIR security monitoring guidance identifies timely detection of adversarial intrusions and unauthorized parameter changes as the most important benefit of continuous monitoring and automated anomaly detection. These security events directly threaten model integrity, potentially causing the model to make harmful decisions without the organization's knowledge. Timely detection enables rapid response before significant damage occurs-this is the highest-value security assurance outcome.
Why A is Wrong: Interpretability and transparency are model design properties that continuous monitoring supports through decision logging but cannot fundamentally improve. Transparency is achieved through model architecture and documentation choices, not monitoring.
Why B is Wrong: Strategic alignment is a governance and design objective. While monitoring can confirm outputs align with intended behavior, it cannot ensure alignment with evolving strategic goals, which requires governance review processes.
Why D is Wrong: Automated risk register and vulnerability database updates are administrative governance benefits that flow from monitoring findings. They represent a useful secondary capability but not the primary security value of continuous monitoring in production.
質問 # 185
Which of the following is the MOST significant risk when an organization depends on a single AI vendor ' s proprietary environment?
正解：B
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. 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.
質問 # 186
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