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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| AI Life Cycle Risk Management | 21% | - AI Design, Development/Procurement, and Documentation - AI Data and Asset Management - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation |
| AI Risk Governance and Framework Integration | 37% | - AI Regulatory Compliance and Legal Considerations - AI Organizational Processes and Alignment - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Policies, Procedures, and Organizational Training - AI Trustworthiness, Ethical and Societal Implications |
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NEW QUESTION # 53
Which of the following metrics BEST indicates false positives in an AI model output?
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. Precision is directly affected by false positives because it measures the proportion of predicted positives that are actually positive. Recall is more sensitive to false negatives, F1 combines both dimensions, and overall accuracy can hide a high false-positive rate. This makes option A, Precision, 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 # 54
Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?
Answer: D
Explanation:
Data asset management for large-scale AI programs involves processing, cataloging, and maintaining vast quantities of structured and unstructured data. AI-powered automation addresses the scalability challenges of manual data management processes.
Why D is Correct: The ISACA AAIR AI capabilities guidance identifies automating data cleaning and metadata tagging as the greatest practical benefit of AI-powered data asset management. Large datasets- often containing millions of records-require consistent preprocessing and cataloging to be usable for AI training and governance. AI automation achieves this at scale, with speed and consistency that manual processes cannot match, improving data quality and discoverability across the organization.
Why A is Wrong: Justifying synthetic data usage is a model development strategy decision, not a data asset management benefit. The justification for synthetic data depends on use case requirements, not AI automation capability.
Why B is Wrong: AI tools can support security monitoring but do not inherently reduce the initial impact of data poisoning or exfiltration attacks. Security outcomes depend on specific defensive AI applications, not general data management automation.
Why C is Wrong: Overfitting identification during model training is a model development monitoring activity. While AI can support training analytics, this is a narrow benefit compared to the broad, scalable data asset management value of automated cleaning and tagging.
NEW QUESTION # 55
Which of the following is the GREATEST risk associated with relying on AI-based analytics for continuous monitoring?
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. AI analytics may misinterpret activity when it lacks contextual understanding, producing false positives or missing meaningful risks. This can directly undermine continuous monitoring decisions, making contextual model error more significant than cost or legacy integration issues. This makes option B, Model errors caused by inability to understand context, 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 # 56
Which of the following is the MOST important reason to include AI-specific risk and compliance clauses in vendor contracts?
Answer: D
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. AI-specific contract clauses should clearly allocate liability and responsibilities for breaches affecting models, data, and services. Baseline frameworks and anonymization duties may be included, but enforceable accountability is the key reason for risk-based contracting. This makes option A, Assignment of liability for breaches impacting models and training 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 # 57
An organization has deployed generative AI tools broadly but lacks a consistent method to refresh governance policies and controls. Which of the following is the risk practitioner's BEST recommendation?
Answer: C
Explanation:
Generative AI capabilities and the associated risk landscape evolve rapidly. Governance policies and controls must be refreshed through a structured, regular process rather than reactively or only when compliance requirements change.
Why A is Correct: According to ISACA AAIR, establishing a regular review cadence with codified reassessment procedures is the most robust approach because it creates a systematic, predictable process for keeping governance current. By documenting when and how policies will be reviewed-including triggers for ad hoc review (new deployments, incidents, regulatory changes)-the organization ensures governance never stagnates regardless of external pressures.
Why B is Wrong: Regulatory alignment is an important input to governance refresh but represents a reactive, external-trigger approach. Relying primarily on regulatory signals means governance lags behind organizational AI changes not covered by new regulations.
Why C is Wrong: Centralizing authority in executive and technical leadership creates decision bottlenecks and reduces the operational agility needed to keep pace with rapidly evolving AI deployments. Distributed governance with clear escalation paths is more effective.
Why D is Wrong: Annual reviews are too infrequent for generative AI tools, which may see significant capability changes and risk profile shifts multiple times per year. Annual compliance audits cannot keep governance current in a rapidly evolving AI environment.
NEW QUESTION # 58
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