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| Section | Weight | Objectives |
|---|---|---|
| AI Auditing Tools and Techniques | 21% | - Audit Planning and Design - AI Audit Outputs and Reporting - Audit Evidence Collection Techniques - Data Quality and Analytics for AI Audit - Audit Testing and Sampling Methodologies |
| AI Governance and Risk | 33% | - Ethics, Regulations, and Standards for AI - AI Models, Considerations, and Requirements - AI Governance and Program Management - AI Risk Management - Privacy and Data Governance Programs |
| AI Operations | 46% | - Third-Party and Supply Chain Risk - Performance Monitoring and Evaluation - AI System Lifecycle and Deployment - Incident Management and Resilience - Operational Controls and Readiness |
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NEW QUESTION # 135
Which of the following is the GREATEST challenge when auditing an AI-based decision-making system?
Answer: C
Explanation:
The "Black Box" nature of many modern AI systems, particularly deep neural networks, makes it difficult to understand exactly how a decision was derived. "Understanding the transparency (and explainability) of AI models" is identified by the AAIATM manual as the greatest audit challenge because it limits the auditor's ability to verify fairness, detect hidden bias, or confirm compliance with logic-based regulations.
NEW QUESTION # 136
During a pre-implementation risk assessment, an AI model is determined to present a significant risk of bias and potential harm in excess of the organization's risk tolerance. Which of the following is the MOST appropriate response?
Answer: B
Explanation:
The AAIATM Study Guide advises that if an AI model presents a risk that exceeds the organization's predefined risk tolerance--especially in cases of ethical harm or bias--deployment should be delayed until proper safeguards are in place. This approach prevents legal exposure and preserves stakeholder trust.
"When AI risks exceed acceptable thresholds, organizations must suspend implementation until corrective action reduces the risk to within tolerance levels. Proceeding without mitigation violates sound governance principles."
NEW QUESTION # 137
An organization deploys an AI recruitment platform to screen job applicants. The IS auditor identifies that the platform's decisions may be influenced by model bias. Which of the following risk mitigation strategies is BEST for the auditor to recommend?
Answer: D
Explanation:
Periodic testing and monitoring for bias is a sustainable, proactive strategy aligned with best practices outlined in the AAIA™ Study Guide. This approach ensures that the AI system remains compliant over time, even as data and hiring conditions change.
"Ongoing fairness assessments help detect emerging biases and ensure that the AI model maintains equitable decision-making standards. Periodic testing also allows organizations to take corrective action before regulatory or reputational damage occurs." Suspending the system (B) or relying solely on external datasets (C) are temporary or limited in scope.
Manual reviews (D) are effective but do not solve the root issue. Therefore, A provides a comprehensive, audit-aligned solution.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Bias Mitigation and Monitoring"
NEW QUESTION # 138
Which of the following is the BEST recommendation to mitigate excessive agency when implementing an AI system as a browser extension?
Answer: A
Explanation:
Excessive agency occurs when AI systems act too autonomously, making decisions without appropriate human oversight. To mitigate this in a browser extension, the BEST approach is to minimize functionality (A), ensuring the extension performs only narrowly defined, controlled actions. This aligns with AAIA's guidance on limiting AI autonomy, applying least-privilege principles, and restricting unnecessary actions.
NEW QUESTION # 139
Which of the following is the MOST frequent cause of generative AI model hallucinations?
Answer: B
Explanation:
Hallucinations-instances where a generative model produces factual errors or nonsensical information with high confidence-are primarily caused by " Inadequate data quality " in the training set. If the model is trained on data that is contradictory, incomplete, or contains " noise " (incorrect facts), it fails to learn accurate semantic relationships. The ISACA AAIA™ manual highlights that " Data Cleaning " and " Provenance " are essential to mitigate this. While weak controls (Option C) or poor change management (Option D) can lead to other risks, the mathematical root of the hallucination itself is typically found in the underlying quality and accuracy of the training data.
NEW QUESTION # 140
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