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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Assurance and Continuous Improvement - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting |
| Topic 3: AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases |
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47. Frage
To reinforce organization-wide ethical norms and risk recognition, which of the following is MOST important to integrate into AI user training?
Antwort: B
Begründung:
Effective AI user training must go beyond policy acknowledgment and compliance instruction to equip employees with the practical skills needed to identify ethical risks and report them appropriately. This builds an active risk-aware workforce.
Why B is Correct: The ISACA AAIR framework identifies that training on ethical risk indicators and reporting mechanisms directly reinforces ethical norms by enabling employees to recognize real-world signs of AI misuse, bias, or harmful outputs. When staff can identify specific risk signals and know how to escalate them, the organization builds a proactive risk culture grounded in practical ethical literacy.
Why A is Wrong: Acceptable use policy acknowledgment is a compliance activity, not a culture-building measure. Acknowledging a document does not ensure employees understand how to apply ethical principles in practice.
Why C is Wrong: Cyber threat identification addresses security risk, which is narrower than the full scope of ethical AI risk. Security training does not develop ethical judgment regarding fairness, bias, or societal impact.
Why D is Wrong: Regulatory compliance checklists address legal obligations but do not develop the ethical reasoning and risk recognition skills needed to reinforce organizational norms.
48. Frage
Which of the following is the MOST suitable key risk indicator (KRI) for model drift resulting in biased or discriminatory outputs?
Antwort: A
Begründung:
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 disparate impact ratio directly measures differences in favorable outcomes across groups and is therefore the most relevant KRI when the stated concern is discriminatory output. Population stability measures distributional drift but does not directly establish disparate treatment. This makes option C, Disparate impact ratio, 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.
49. Frage
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?
Antwort: B
Begründung:
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.
50. Frage
Which of the following would be of GREATEST concern to a risk practitioner reviewing an AI acceptable use policy for an organization that operates in a highly regulated environment?
Antwort: A
Begründung:
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. In a highly regulated environment, an acceptable use policy without explicit privacy rules leaves users without clear limits for entering, processing, storing, or sharing regulated information through AI systems. This creates direct compliance and data-protection exposure. This makes option B, Lack of explicit data privacy rules and best practices, 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.
51. Frage
Which of the following BEST enables an organization to comply with regulations on the use of biometric data by its AI models?
Antwort: B
Begründung:
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. Biometric compliance requires governance over purpose, consent, access, retention, and authorized use. Liveness detection and vulnerability scanning improve security, but purpose-based governance is what prevents sensitive biometric data from being repurposed beyond approved uses. This makes option D, Implementing strong governance to ensure biometric data is used only for its intended purposes, 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.
52. Frage
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