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| Section | Objectives |
|---|---|
| Topic 1: AI Risk Management | - Risk identification and assessment for AI systems
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| Topic 2: Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
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| Topic 3: AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| Topic 4: AI Lifecycle Controls | - Controls across AI development lifecycle
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| Topic 5: Regulatory and Compliance Requirements | - Global AI regulatory landscape
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NEW QUESTION # 57
Which of the following should be the PRIMARY consideration when determining the priority for restoration of AI systems following a model exfiltration attack?
Answer: C
Explanation:
Following a model exfiltration attack, multiple AI systems may require restoration. Prioritization must be based on objective criteria that reflect the potential business impact of continued unavailability. Systems supporting critical business functions must be restored before those supporting non-critical functions.
Why A is Correct: According to ISACA AAIR business continuity guidance for AI, the primary criterion for restoration priority is the AI system's criticality to business requirements. Systems that support mission- critical functions-patient care, financial transaction processing, safety operations-represent the highest restoration priority because their unavailability causes the greatest operational harm. This risk-based prioritization framework is consistent with standard business continuity management principles applied to the AI context.
Why B is Wrong: Team member expertise affects restoration capacity and speed but should not drive prioritization decisions. Priority is determined by business impact, not by where the team has the most technical capability. Resource allocation follows priority, not the reverse.
Why C is Wrong: Vulnerability testing and patch costs are operational considerations that may influence restoration timelines but should not override business criticality in determining priority. Cost-based prioritization could lead to restoring cheaper but less critical systems first.
Why D is Wrong: Dataset availability affects the feasibility and timeline of model retraining but is a logistical consideration rather than the primary basis for restoration priority. Critical systems should be prioritized even if their restoration is technically more complex.
NEW QUESTION # 58
In the context of AI risk mitigation, which of the following is the PRIMARY reason to have rollback capabilities in place for AI system updates?
Answer: C
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. Rollback capability allows the organization to restore a previously validated state when an AI update causes unintended consequences such as degraded accuracy, new bias, control failures, or unsafe behavior. It is a resilience and change-management safeguard. This makes option B, To correct unintended consequences, 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 # 59
Which of the following is the GREATEST risk when organizational risk tolerance is not embedded into AI 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. If risk tolerance is not embedded in AI policies, different teams may make inconsistent decisions and permit impacts that exceed enterprise-approved limits. This weakens escalation, treatment, and acceptance decisions across the AI portfolio. This makes option C, Inconsistent governance decisions that fail to mitigate unacceptable impacts, 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 # 60
Which of the following is the MOST effective approach to help ensure compliance with AI acceptable use policies?
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. 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 # 61
A risk practitioner is evaluating AI model cards and documentation prior to deployment. Which of the following represents the GREATEST risk to enterprise AI governance?
Answer: B
Explanation:
AI governance depends on the ability of stakeholders to understand, audit, and oversee AI model decisions.
Explainability is the technical and documentation property that enables this oversight. When model cards fail to adequately document explainability, the entire governance chain is compromised.
Why B is Correct: According to ISACA AAIR, inadequate explainability in model documentation is the greatest governance risk because it prevents risk practitioners, auditors, regulators, and business owners from understanding why a model produces its outputs. Without explainability, discriminatory or erroneous decisions cannot be identified, challenged, or corrected. This undermines accountability, compliance, and responsible AI governance at the enterprise level.
Why A is Wrong: Regulatory filing delays represent a compliance timing issue that can be remediated. While risky, they do not fundamentally compromise the governance capability of understanding and overseeing AI behavior.
Why C is Wrong: Decentralized version control creates configuration management challenges and audit trail gaps. These are significant but can be remediated through governance process improvements. Explainability gaps affect the underlying ability to govern the model itself.
Why D is Wrong: Overly detailed technical specifications represent a documentation quality issue that may reduce usability but does not create a governance risk. Excessive detail is easily distilled; absent explainability cannot be reconstructed after the fact.
NEW QUESTION # 62
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