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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.
Topic 2
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 3
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.

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ISACA Advanced in AI Audit Sample Questions (Q141-Q146):

NEW QUESTION # 141
When an auditor is using AI to test controls, what would be the HIGHEST risk to the audit's integrity?

Answer: C

Explanation:
The ISACA AAIATM framework highlights that the use of AI in auditing does not relieve the auditor of their responsibility for professional judgment. The highest risk is "Over-reliance" or
"Automation Bias," where the auditor accepts AI-generated conclusions without independent validation. If the AI makes a false conclusion due to a hallucination or biased logic, and the auditor fails to "check under the hood," the entire audit report becomes unreliable.


NEW QUESTION # 142
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


NEW QUESTION # 143
An IS auditor is considering the integration of AI techniques into the audit sampling process.
Which of the following BEST enables the auditor to identify high-risk transactions within large data sets for targeted sampling?

Answer: A

Explanation:
Predictive analytics is the most effective method for identifying high-risk transactions because it uses statistical models, anomaly detection, and machine learning to:
Rank transactions by inherent and residual risk
Detect hidden patterns that auditors cannot manually identify
Highlight unusual transaction profiles, outliers, and red flags
Prioritize transactions that require deeper inspection
AAIA's audit domain emphasizes risk-based sampling enhanced by AI, where predictive models significantly improve coverage and accuracy.


NEW QUESTION # 144
While evaluating a complex machine learning (ML) model used for regulatory compliance in a financial institution, which of the following should the IS auditor do to BEST ensure transparency?

Answer: A


NEW QUESTION # 145
When initiating an AI governance program, which of the following is MOST critical to ensure the AI system aligns with organizational objectives and stakeholder needs?

Answer: A

Explanation:
Effective AI governance begins with " Problem Framing " and process identification. Before acquiring hardware (Option B) or setting up monitoring (Option C), the organization must clearly define the specific business processes the AI will augment or automate. This ensures that the development of the model is rooted in strategic objectives rather than technological hype. The ISACA AAIA™ framework emphasizes that governance structures must first validate the " use case " to ensure it is ethical, feasible, and value-adding.
Without a well-defined use case, the organization risks implementing AI that is legally compliant (Option A) but operationally irrelevant or harmful to its core mission.


NEW QUESTION # 146
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