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ISACA AAIA Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Auditing Tools and Techniques
Topic 2: AI Operations
Topic 3: AI Governance and Risk

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ISACA Advanced in AI Audit Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is the GREATEST risk of relying solely on aggregate accuracy as the primary success metric for a deployed AI model?

Answer: C

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 # 30
From an audit perspective, which of the following BEST supports the objectives of an AI governance program?

Answer: C

Explanation:
While roles (Option A), committees (Option B), and registers (Option C) are essential organizational components, the "AI Policies" themselves are the most comprehensive support for governance objectives. Policies provide the "enforceable standards" for how AI must be built and managed. Specifically, policies addressing interpretability, transparency, and resiliency ensure that the organization's AI systems are auditable, fair, and stable. According to ISACA, the governance program must be rooted in these technical and ethical pillars to provide the necessary guardrails for AI development. Policies act as the "Source of Truth" that guides committees and stakeholders in their decision-making processes.


NEW QUESTION # 31
Which of the following is the PRIMARY objective of performing adversarial testing on AI models?

Answer: B

Explanation:
Adversarial testing involves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. The primary objective is to discover weaknesses and control gaps (D) in the model and its surrounding processes--such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.


NEW QUESTION # 32
Which of the following will provide the BEST evidence to support the alignment of an AI model with an organization's business objectives?

Answer: D


NEW QUESTION # 33
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?

Answer: B

Explanation:
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing.
According to the AAIATM Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made--false positives and false negatives--thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed--not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.


NEW QUESTION # 34
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