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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 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.
Topic 3
  • 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.

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ISACA Advanced in AI Audit Sample Questions (Q175-Q180):

NEW QUESTION # 175
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 AAIATM 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 # 176
An organization is using information gathered from customer accounts to train its AI chatbot. Which of the following is the GREATEST risk associated with this practice?

Answer: A


NEW QUESTION # 177
An organization's AI model relies on synthetic data to augment a small training dataset. What is the PRIMARY audit consideration?

Answer: B

Explanation:
Synthetic data generation methods can inadvertently encode or amplify biases present in the seed data, or fail to capture rare real-world scenarios, undermining model reliability if not properly validated.


NEW QUESTION # 178
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?

Answer: D

Explanation:
To determine whether an AI model's outputs areeffectively controlled for bias, an auditor needsempirical performance evidenceacross demographic groups.Similar accuracy ranges across groups (A)demonstrate that the model performs equitably and does not disproportionately advantage or disadvantage specific populations. This aligns with AAIA's emphasis onfairness metrics, such as disparate error rates, equal opportunity, and demographic parity analyses.
Option B (fairness definition) is important for governance but does not prove fairness in practice. Option C may introduce historical human bias rather than mitigate it. Option D (transparency of development) supports explainability but does not validate fairness outcomes. Actualperformance parity across demographic groupsis the strongest evidence of bias control.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (fairness evaluation; bias assessments).
ISACA fairness guidance emphasizing cross-group performance comparison.


NEW QUESTION # 179
Which of the following should be an IS auditor's GREATEST concern when using a predictive AI tool to analyze data abnormalities?

Answer: B

Explanation:
For apredictive AI tool analyzing abnormalities, the GREATEST concern is therate and impact of false positives and false negatives(A). False positives can lead to unnecessary investigation, while false negatives mean true issues (e.g., fraud, control failures) remain undetected. From an assurance perspective, false negatives are especially critical because they directly undermine audit objectives. AAIA underscores that key performance metrics (e.g., precision, recall) and error trade-offs are essential in evaluating AI tools used in audit.
Integration ease (B), speed (C), and cost (D) are important practical considerations but are secondary to whether the toolaccurately identifies or misses significant anomalies. Therefore, error behavior-false positives and false negatives-represents the primary risk to audit quality.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI in Audit Processes; Domain 2: AI Operations (model performance metrics and risk).
ISACA analytics guidance on evaluating AI tools using precision, recall, and error analysis in audit contexts.


NEW QUESTION # 180
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