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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement - AI Risk Monitoring and Reporting |
| AI Risk Governance and Framework Integration | 37% | - AI Organizational Processes and Alignment - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability |
| AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management - AI Model Training, Testing, and Validation |
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NEW QUESTION # 83
Which of the following is the MOST important consideration when assessing risk for an AI system provided by a third party, as opposed to a non-AI related system?
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. Third-party AI requires greater continuous monitoring because models, data, dependencies, and provider behavior can change over time. Traditional onboarding assessments alone may not detect drift, updates, or newly introduced AI-specific risk. This makes option C, Greater emphasis on continuous monitoring due to the dynamically evolving nature of AI models, 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 # 84
An organization uses AI to generate procedure documents for operational processes. Which of the following would be of GREATEST concern to a risk practitioner?
Answer: A
Explanation:
AI-generated content-including operational procedures-can contain errors, omissions, hallucinations, and contextually inappropriate guidance. Human review is a critical quality control and accountability mechanism that ensures generated procedures are accurate, complete, and appropriate for actual operational use.
Why A is Correct: The ISACA AAIR guidance on human oversight identifies the absence of human review as the greatest risk in AI-generated documentation. Without review, errors and AI hallucinations are propagated directly into operational use, potentially causing safety incidents, compliance violations, or operational failures. Human review is the last line of defense against AI output quality failures, particularly in operational procedure contexts where incorrect instructions can have serious consequences.
Why B is Wrong: Outdated procedures are a content quality issue that would typically be caught during human review. The greater concern is that no review is occurring, which allows all types of errors-including outdated content-to reach operational use unchallenged.
Why C is Wrong: Policy misalignment is a governance concern but represents a specific type of error that would be identified if adequate human review were performed. The absence of review is the root governance failure.
Why D is Wrong: Using AI to generate procedures for high-risk activities is a deployment scope concern that raises the stakes of errors. However, the fundamental governance failure-and the greatest concern-is that no human verification occurs regardless of the risk level of the activity.
NEW QUESTION # 85
Which of the following is the MOST effective approach to help ensure compliance with AI acceptable use policies?
Answer: C
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 # 86
Which of the following is the PRIMARY benefit of integrating AI risk processes into an enterprise risk framework?
Answer: B
Explanation:
Enterprise risk framework integration elevates AI risk management from a technical discipline to a strategic organizational function, ensuring AI risks are considered alongside all other enterprise risks in strategic planning and decision-making.
Why D is Correct: The ISACA AAIR curriculum identifies enterprise integration as the mechanism that enables organization-level oversight and ensures AI risk management aligns with strategic objectives, risk appetite, and governance structures. This integration allows the board and senior management to make informed decisions about AI investment, deployment, and risk acceptance with full awareness of AI's contribution to the organizational risk profile.
Why A is Wrong: KPI benchmarking is an operational performance management activity. While integration may improve KPI accuracy, this is a secondary operational benefit rather than the primary strategic benefit of ERM integration.
Why B is Wrong: Regulatory compliance is improved by integration but represents a specific compliance benefit rather than the primary organizational value. Compliance is an output of good governance, not the purpose of ERM integration.
Why C is Wrong: Cyber threat identification is a security function that benefits from integration but is not the primary benefit. Many AI risks are non-cyber in nature-fairness, accuracy, transparency-and would not be captured by a cyber-focused framing.
NEW QUESTION # 87
An organization plans to deploy an AI system that ingests multiple sources with varying completeness and accuracy. Which of the following is the risk practitioner's BEST recommendation?
Answer: C
Explanation:
Data quality directly determines AI model accuracy and reliability. When input sources vary in completeness and accuracy, the AI system is exposed to continuous data quality risks that can produce unreliable outputs.
This requires ongoing, real-time quality management rather than periodic or reactive responses.
Why C is Correct: According to ISACA AAIR data quality guidance, implementing continuous real-time QA processes is the most effective approach for managing variable-quality multi-source inputs. Real-time QA identifies and addresses quality issues as data enters the system-before they contaminate model inputs and outputs. This prevents quality problems from accumulating and ensures the model consistently receives the highest-quality available data.
Why A is Wrong: Synthetic data augmentation is useful for addressing data scarcity but does not resolve accuracy and completeness issues in existing real-world sources. Generating synthetic data alongside poor- quality real data does not improve the real data.
Why B is Wrong: Post-implementation assessments are reactive-they identify problems after they have already affected model behavior and potentially produced harmful outputs. Prevention through real-time QA is superior to post-hoc remediation.
Why D is Wrong: Fine-tuning model parameters can improve robustness to input variation but does not address underlying data quality problems. Models trained to tolerate poor data may produce less reliable outputs than models receiving consistently high-quality data.
NEW QUESTION # 88
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