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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Policies, Procedures, and Organizational Training - AI Regulatory Compliance and Legal Considerations - AI Trustworthiness, Ethical and Societal Implications - AI Models, Frameworks, Strategies, and Use Cases - AI Ownership, Oversight, and Accountability - AI Organizational Processes and Alignment |
| AI Life Cycle Risk Management | 21% | - AI Model Training, Testing, and Validation - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Data and Asset Management |
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement - AI Risk Identification and Assessment |
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NEW QUESTION # 68
A risk practitioner assesses a new AI system and determines that the risk is within the organization's risk tolerance. Which of the following is the BEST recommendation to ensure system controls remain effective over time?
Answer: C
Explanation:
Even when an AI system is initially assessed as within risk tolerance, its risk profile evolves as the system encounters new data, the operational environment changes, and model performance drifts. Controls that were effective at deployment may become insufficient as these changes accumulate.
Why C is Correct: The ISACA AAIR operational monitoring guidance identifies continuous monitoring for data and performance drift as the most important mechanism for maintaining control effectiveness over time.
Drift detection provides early warning when the AI system begins behaving differently from its validated state-enabling timely control adjustments before risk tolerance is breached. This is particularly critical because AI systems can degrade gradually in ways not visible without active monitoring.
Why A is Wrong: Framework alignment establishes the control baseline but does not actively verify that controls remain effective as the system evolves. Frameworks provide structure; monitoring provides assurance.
Why B is Wrong: Security and risk awareness training is an important human capability development activity but does not detect technical changes in AI system behavior. Training does not substitute for technical monitoring.
Why D is Wrong: Periodic compliance reviews occur at scheduled intervals and may miss drift that develops between review cycles. Continuous monitoring provides real-time detection that periodic reviews cannot match.
NEW QUESTION # 69
Which of the following AI system considerations BEST mitigates risk associated with model drift?
Answer: C
Explanation:
Model drift occurs when the statistical relationship between model inputs and outputs changes over time, causing previously accurate predictions to become less reliable. Regular retraining with updated, relevant data recalibrates the model to current real-world patterns.
Why A is Correct: According to ISACA AAIR model maintenance guidance, regular retraining with new relevant datasets is the most direct mitigation for model drift. By periodically retraining on current data, the model learns the latest patterns and relationships-counteracting the drift that accumulates as real-world conditions diverge from the original training data. This is the standard industry practice for maintaining production AI models in dynamic environments.
Why B is Wrong: Restricting automated data validation to low-risk models creates a governance double standard that leaves high-risk models more vulnerable. If anything, high-risk models require more rigorous automated validation, not less. This approach increases rather than mitigates drift risk for critical applications.
Why C is Wrong: Maintaining existing dataset variance during preprocessing preserves statistical characteristics from a historical snapshot. If drift has occurred in real-world data, deliberately maintaining old variance levels prevents the model from adapting to new conditions.
Why D is Wrong: Role-based access controls protect model parameters and data from unauthorized modification. While important for security, access controls do not address model drift, which is driven by changing real-world conditions rather than unauthorized changes.
NEW QUESTION # 70
Which of the following MOST effectively reduces bias resulting from variances in free-form answers in the dataset?
Answer: A
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. Free-form data can contain inconsistent formats, scales, terminology, and representations that distort model learning.
Scaling and standardizing inputs reduces unwanted variance and helps prevent irrelevant formatting differences from becoming sources of bias. This makes option C, Scaling and standardizing inputs, 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 # 71
Which of the following is MOST important to verify when assessing open-source AI components in an organization's supply chain?
Answer: B
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. Open-source AI components should be traceable so the organization can verify origin, dependencies, integrity, licensing, and maintenance history. Geography and cost are secondary, and documentation ownership alone does not establish trustworthy provenance. This makes option A, Traceability for open-source AI artifacts, 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 # 72
Which of the following should be the PRIMARY consideration when developing AI risk awareness training?
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. AI risk awareness training must remain current because threats, attack techniques, model capabilities, and regulatory expectations evolve rapidly. Static training quickly becomes obsolete, so content should be refreshed to reflect emerging AI-specific risks and attack patterns. This makes option B, Ensuring contents are regularly updated to reflect new AI threats, 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 # 73
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