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ISACA AAIA Exam Syllabus Topics:

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

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ISACA Advanced in AI Audit Sample Questions (Q287-Q292):

NEW QUESTION # 287
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: A

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 # 288
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: D


NEW QUESTION # 289
Which of the following is the PRIMARY purpose of an AI acceptable use policy?

Answer: C

Explanation:
An AI acceptable use policy (AUP) defines how AI tools and technologies should be ethically and responsibly used within an organization. According to the AAIATM Study Guide, the primary goal of an AUP is to prevent misuse and promote adherence to ethical, legal, and operational standards.
"An AI acceptable use policy provides governance over how AI tools may be used, especially regarding data handling, fairness, and prohibited uses. It aligns employee actions with organizational values and compliance requirements."


NEW QUESTION # 290
The PRIMARY benefit of using AI in audit fieldwork is that it:

Answer: D

Explanation:
According to the AAIATM manual, the transformative power of AI in audit fieldwork lies in its ability to process 100% of a data population at scale. While traditional audit methods rely on manual sampling, which may miss infrequent or complex outliers, AI-driven tools can efficiently scan massive, multi-dimensional datasets to pinpoint anomalies and emerging trends that warrant further investigation. This increases the depth of the audit and allows for a more rigorous risk- based approach.


NEW QUESTION # 291
Which of the following is the MOST significant benefit of performing frequent AI model testing and retraining?

Answer: C

Explanation:
AI models are not " static " systems; they are reflections of the data they were trained on. In dynamic environments, data patterns change constantly (e.g., market trends or new fraud tactics). Frequent testing and retraining ensure the model is " updated with current data, " which prevents performance degradation and " data drift. " While testing can assess attacks (Option A), the primary operational reason for a recurring retraining schedule is to maintain the model ' s relevance and accuracy against the most recent real-world behaviors.


NEW QUESTION # 292
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