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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Models, Frameworks, Strategies, and Use Cases |
| Topic 2: AI Risk Program Management | 42% | - AI Risk Monitoring and Reporting - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Assurance and Continuous Improvement |
| Topic 3: AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Model Training, Testing, and Validation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management |
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NEW QUESTION # 83
A risk practitioner is evaluating training datasets for a new AI model. Which of the following approaches BEST reduces fairness risk during model development?
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.
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.
NEW QUESTION # 84
A risk practitioner assesses an AI model used for predictive diagnostics and finds that the system lacks automated update mechanisms. Which of the following is the GREATEST risk?
Answer: D
NEW QUESTION # 85
A risk practitioner is reviewing an organization's implementation of a business-critical AI decision system.
Which of the following would be of GREATEST concern?
Answer: A
Explanation:
Business-critical AI decision systems require comprehensive testing of failure modes and recovery procedures before deployment. For systems making consequential decisions, untested failure scenarios create significant operational, financial, and reputational risks when failures occur in production.
Why C is Correct: The ISACA AAIR testing and validation guidance identifies insufficient scenario-based failure mode testing as the greatest concern for business-critical AI. Without testing how the system behaves when it fails-what recovery procedures activate, how human oversight is engaged, how data integrity is maintained during failures-organizations cannot be confident the system can be safely operated through failures. For critical systems, untested failure scenarios represent unacceptable operational risk.
Why A is Wrong: Conventional security providers may require AI-specific expertise supplements but represent an operational security management concern rather than the greatest risk to system reliability and safety. Security monitoring can be supplemented without fundamentally threatening critical system operations.
Why B is Wrong: Cross-functional incident training gaps are a significant organizational preparedness concern but represent a human capability gap that can be addressed through training programs. The system design risk of untested failure modes is more fundamental.
Why D is Wrong: Not requiring 100% decision accuracy is appropriate risk tolerance calibration-no AI system achieves perfect accuracy, and setting realistic thresholds is a sign of mature risk governance. This reflects sound risk acceptance practice rather than a governance concern.
NEW QUESTION # 86
Which of the following BEST mitigates the risk of misaligned return on investment (ROI) in AI initiatives?
Answer: B
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. Mapping AI project outcomes to enterprise performance metrics ties investment to measurable organizational value. An inventory improves visibility and workforce metrics measure only one dimension, while a narrow focus on quantifiable benefits can miss strategic value. This makes option C, Mapping AI project outcomes to enterprise performance metrics, 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 # 87
An organization is selecting an AI model for a solution that requires the creation of new content. It is MOST important to consider selecting:
Answer: A
Explanation:
Different AI model architectures are optimized for different tasks. Content creation requires a model that can generate novel outputs-text, images, audio, or code-rather than classify, cluster, or optimize decisions based on rules or rewards.
Why A is Correct: According to ISACA AAIR AI technology selection guidance, generative models are specifically designed to synthesize new content by learning the underlying probability distributions of training data. They can produce novel, contextually appropriate outputs-exactly what content creation requires.
Large language models (LLMs), diffusion models, and GANs are generative architectures designed for this purpose.
Why B is Wrong: Unsupervised clustering groups existing data points by similarity but does not generate new content. It is used for pattern discovery and segmentation, not creative output generation.
Why C is Wrong: Rule-based expert systems execute predefined logic trees and cannot produce novel content beyond the rules explicitly encoded. They are rigid, deterministic systems unsuitable for open-ended content creation.
Why D is Wrong: Reinforcement learning optimizes decision sequences to maximize cumulative rewards. It is suited for sequential decision-making tasks (games, robotics, recommendation systems) but is not the appropriate architecture for direct content generation.
NEW QUESTION # 88
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