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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% | - AI System Lifecycle and Deployment - Performance Monitoring and Evaluation - Operational Controls and Readiness - Incident Management and Resilience - Third-Party and Supply Chain Risk |
| AI Auditing Tools and Techniques | 21% | - Audit Testing and Sampling Methodologies - Audit Evidence Collection Techniques - AI Audit Outputs and Reporting - Audit Planning and Design - Data Quality and Analytics for AI Audit |
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NEW QUESTION # 128
An IS auditor is reviewing AI-driven processes that automate financial approvals. Which of the following poses the GREATEST challenge to maintaining separation of duties (SOD)?
Answer: C
Explanation:
Separation of Duties (SOD) is intended to prevent errors or fraud by ensuring no single person (or entity) has control over all stages of a transaction. When AI automates financial approvals, the
"Lack of transparency in the algorithm" creates a challenge because the logic that grants approval is often opaque. If the algorithm functions as a "Black Box," it may consolidate decision- making power that was previously spread across multiple human roles, without sufficient visibility into how those decisions are made. This can lead to unauthorized or erroneous approvals going undetected. Effective AI governance must ensure that automated approvals are subject to transparent audit trails and secondary human reviews to uphold SOD principles.
NEW QUESTION # 129
Which of the following metrics are the BEST indication of a mature and effective approach to an organization's data governance program for its AI systems?
Answer: C
NEW QUESTION # 130
The PRIMARY purpose of maintaining an audit trail in AI systems is to:
Answer: B
Explanation:
Audit trails in AI systems serve to document the inputs, processes, and outputs of AI decisions, allowing stakeholders and auditors to trace how decisions were made. The AAIA™ Study Guide identifies transparency and traceability as the core functions of an AI audit trail.
"Maintaining audit logs is critical to explainability and accountability. It ensures that decisions made by AI systems can be reconstructed and assessed for accuracy, ethics, and legality." Although compliance (D) is an outcome and fairness (B) a goal, only A captures the foundational purpose of audit trails in AI systems.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Auditability and Logging Mechanisms"
NEW QUESTION # 131
For a sales promotion, an AI system sorts customer attributes into several categories by analyzing transaction history. Verifying which of the following would BEST validate the effectiveness of this process?
Answer: A
Explanation:
The effectiveness of an AI-driven business process--such as categorizing customers for promotional campaigns--depends on how well it supports defined business objectives. The AAIATM Study Guide recommends validating that AI methodology aligns with intended outcomes as part of performance auditing.
"Effectiveness is best measured by assessing whether the AI logic contributes meaningfully to business goals. Output alignment with organizational KPIs or campaign strategies provides clear evidence of functional success."
NEW QUESTION # 132
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
Answer: D
Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations--where generative models produce fabricated or misleading outputs.
The AAIATM Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
NEW QUESTION # 133
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