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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Life Cycle Risk Management | 21% | - AI Model Training, Testing, and Validation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Design, Development/Procurement, and Documentation |
| Topic 2: AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Models, Frameworks, Strategies, and Use Cases - AI Trustworthiness, Ethical and Societal Implications - AI Regulatory Compliance and Legal Considerations |
| Topic 3: AI Risk Program Management | 42% | - AI Risk Assurance and Continuous Improvement - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation |
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NEW QUESTION # 94
An organization runs AI workloads on shared cloud infrastructure. Which of the following is the MOST significant risk?
Answer: C
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. Shared cloud infrastructure creates confidentiality and integrity exposure for model weights and data. Unauthorized access can disclose proprietary models, sensitive training information, or customer data, making this more significant than cost, latency, or scaling concerns. This makes option B, Unauthorized access to model weights and data, 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 # 95
Which of the following is the BEST risk response when an organization identifies that ethical and legal exposures from a new non-critical AI system exceeds its risk tolerance?
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. When ethical and legal exposures exceed tolerance and the AI system is non-critical, avoidance is the most proportionate response. Acceptance is inappropriate above tolerance, transfer cannot remove all accountability, and mitigation is suitable only if effective controls can reduce exposure sufficiently. This makes option D, Avoidance, 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 # 96
Which of the following BEST helps to ensure an AI system ' s potential human rights harms are managed?
Answer: A
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 # 97
Which of the following is the PRIMARY benefit of implementing a formal data disposition process for AI training and operational datasets?
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. A formal data disposition process reduces unnecessary retention of AI training and operational data, thereby lowering the amount of sensitive information exposed to breach and reducing noncompliance with retention or data- minimization obligations. This makes option D, Reducing exposure to breaches and regulatory penalties, 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 # 98
Which of the following is the BEST way for a risk practitioner to communicate rapidly evolving changes to the organization ' s risk profile resulting from the use of generative AI?
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. Rapidly changing generative-AI risk is best communicated through concrete real-world incidents and business impacts that stakeholders can understand. Technical root causes or exhaustive lists can obscure the practical consequences that decision-makers need to evaluate. This makes option B, Provide real-world examples of risk impacts from generative AI incidents and breaches, 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 # 99
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