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| Section | Weight | Objectives |
|---|---|---|
| AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Policies, Procedures, and Organizational Training - AI Ownership, Oversight, and Accountability - AI Regulatory Compliance and Legal Considerations - AI Models, Frameworks, Strategies, and Use Cases |
| 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 |
| 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 |
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NEW QUESTION # 121
Which of the following is the GREATEST risk when an AI model is trained on multiple data sources that differ in update frequency and quality?
Answer: B
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. Training from sources with different update frequencies and quality can introduce conflicting, stale, or incomplete information, leading to inconsistent model outputs and operational decisions. Cost and integration issues are secondary to unreliable decision quality. This makes option A, Inconsistency in model outputs and operational decisions, 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 # 122
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: A
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 # 123
Which of the following metrics BEST indicates false positives in an AI model output?
Answer: B
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 # 124
An organization uses an AI model that learns from live data streams. Which of the following is the BEST course of action to manage the risk of an adaptive model?
Answer: A
Explanation:
AI models that learn from live data streams continuously update their parameters based on incoming data.
This creates two specific risks: the model's behavior may drift from its validated state as data patterns change (data drift), and adversaries may deliberately introduce malicious data to manipulate the model's learning (data poisoning).
Why D is Correct: According to ISACA AAIR adaptive model risk guidance, implementing automated monitoring for both data drift and data poisoning is the most comprehensive response to live-learning model risks. Automated monitoring operates continuously at the speed of the data stream, detecting statistical changes in input distributions (drift signals) and anomalous data patterns (poisoning signals) in real time- enabling timely intervention before either risk materializes into harmful behavior.
Why A is Wrong: Defense-in-depth for model access controls who can interact with the model but does not address risks arising from the data the model learns from. Access controls are necessary but insufficient for managing adaptive learning risks.
Why B is Wrong: Restricting data sources reduces learning breadth, potentially undermining the model's adaptive capability that creates its value. Periodic inspections are too infrequent for live-learning systems where risks can emerge between inspection cycles.
Why C is Wrong: Dynamic performance thresholds detect output degradation after drift has occurred. While useful as a safety net, this reactive monitoring does not prevent drift or detect poisoning early enough for the live-learning risk context.
NEW QUESTION # 125
Which of the following is the GREATEST organizational risk when AI performance alerts are not escalated to decision-makers for review and decisioning?
Answer: D
Explanation:
AI performance alerts signal emerging issues with model behavior-accuracy degradation, anomalous outputs, drift-that require prompt management attention and decision-making. When these alerts are not escalated, corrective actions are delayed and AI system instability can escalate into serious operational incidents.
Why B is Correct: The ISACA AAIR operational risk management guidance identifies business disruption from delayed remediation as the greatest risk from alert escalation failures. When performance alerts are suppressed or not acted upon, unstable AI behavior continues and potentially worsens until it produces visible failures-system outages, incorrect critical decisions, customer harm-that disrupt business operations. The gap between alert generation and remediation is the window during which the AI system can cause the most damage.
Why A is Wrong: Governance reporting gaps represent a compliance and oversight concern but are secondary to the operational reality of unstable AI causing business disruption. Reporting gaps are administrative failures; operational disruption is the consequential business harm.
Why C is Wrong: Redundant mitigation activities might arise when issues are addressed without coordination, but this is an efficiency concern. The greater risk is that without escalation, no mitigation activities are initiated at all-the opposite of redundancy.
Why D is Wrong: Decision logging gaps affect traceability and auditability. While important for governance purposes, logging failures do not represent the most immediate operational risk from failing to escalate performance alerts to decision-makers.
NEW QUESTION # 126
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