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| Section | Weight | Objectives |
|---|---|---|
| AI Governance and Risk | 33% | - AI Governance and Program Management - Ethics, Regulations, and Standards for AI - Privacy and Data Governance Programs - AI Models, Considerations, and Requirements - AI Risk Management |
| AI Operations | 46% | - Incident Management and Resilience - AI System Lifecycle and Deployment - Operational Controls and Readiness - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation |
| AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies |
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NEW QUESTION # 320
What is the GREATEST risk of relying solely on aggregate accuracy as the primary success metric for a deployed AI model?
Answer: D
Explanation:
Aggregate accuracy can be high overall while performance varies significantly for specific subpopulations or edge cases -- a key reason disaggregated and fairness-specific metrics are also needed.
NEW QUESTION # 321
Which control is MOST important to verify in order to ensure proper data management with AI systems?
Answer: D
Explanation:
According to the ISACA AAIATM Study Guide, a "Data Governance Framework" is the foundational control. It provides the policies, roles (stewards, owners), and standards necessary to manage the entire data lifecycle. Without a framework, activities like inventorying (Option C) or labeling (Option D) are ad-hoc and lack accountability. A formal framework ensures that data management is consistent, compliant with privacy laws, and aligned with the organization's risk tolerance. It is the "enabling" control that makes all other data quality and security metrics meaningful and enforceable.
NEW QUESTION # 322
Which of the following is the MOST important reason to perform regular ethical reviews of AI systems?
Answer: C
Explanation:
The AAIATM Study Guide reinforces that regular ethical reviews are essential to uphold human rights, prevent discriminatory outcomes, and ensure systems function within the boundaries of fairness and legality. While aligning with values (B) and preventing drift (D) are secondary benefits, the primary ethical imperative is the protection of individuals' rights and freedoms.
"Ethical reviews ensure AI systems do not violate rights related to privacy, fairness, access, and due process. This is foundational in building public trust and avoiding legal liabilities." Option C is the clearest expression of this responsibility. Performance and alignment with values are important but secondary to ensuring human-centric safeguards.
NEW QUESTION # 323
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?
Answer: C
Explanation:
The risk described is thatcustomer-facing suggestionsmay be inappropriate, insensitive, or offensive. The BEST mitigation is toperform testing of diverse scenarios(B)-including edge cases, demographic variations, and sensitive contexts-to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlightsscenario-based testingandfairness/impact assessmentsas key practices, especially where recommendations directly influence customer interactions.
Increasing data volume (A) does not ensure fairness or sensitivity. Monitoring servers (C) focuses on technical health, not content appropriateness. Threat analysis (D) is important for security but does not directly address emotional or ethical impacts of model outputs. Therefore, structured,diverse scenario testing is the most targeted and effective approach.
References:
ISACA,AAIA Exam Content Outline- Domain 2 & Domain 5: Testing techniques; ethical and user-impact considerations.
ISACA AI guidance on scenario testing for fairness, appropriateness, and user impact.
NEW QUESTION # 324
Which of the following is the BEST key performance indicator (KPI) to measure a model's performance on imbalanced datasets?
Answer: A
Explanation:
For imbalanced datasets, standard "accuracy" or "error rate" is misleading (e.g., if 99% of transactions are legitimate, a model that predicts "legitimate" every time is 99% accurate but useless for fraud detection). The "F1 score" is the harmonic mean of Precision and Recall, providing a balanced metric that considers both false positives and false negatives. It is the industry-standard KPI for evaluating how well a model handles the minority class. While Precision and Recall are valuable individually, the F1 score provides a single, comprehensive measure of the model's effectiveness in skewed environments.
NEW QUESTION # 325
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