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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q121-Q126):

NEW QUESTION # 121
An IS auditor notes that an AI model achieved significantly better results on training data than on test dat a. Which of the following problems with the model has the IS auditor identified?

A. Overfitting
B. Generalization
C. Underfitting
D. Bias

Answer: A

NEW QUESTION # 122
Which of the following should be of GREATEST concern to an IS auditor when reviewing ethical considerations for an AI solution?

A. The model has not been retrained recently.
B. The solution documentation is still in draft.
C. The decision-making process is unexplainable.
D. The solution is hosted on a shared cloud environment.

Answer: C

Explanation:
TheGREATEST concernis when the AI system'sdecision-making process is unexplainable(A), especially for high-impact or regulated decisions. AAIA stresses that explainability is essential for accountability, fairness assessments, compliance, and public trust. If decisions cannot be explained, the organization cannot validate fairness, detect bias, or justify outcomes to regulators or affected individuals.
Cloud hosting (B) is manageable through standard controls. Retraining frequency (C) affects performance but not core ethics. Draft documentation (D) is a procedural issue, not an ethical barrier. Unexplainable decision logic is thefoundational ethical risk.
References:
ISACA,AAIA Exam Content Outline- Domain 5: Ethical and Legal Considerations in AI (explainability, accountability).

NEW QUESTION # 123
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?

A. The frequency of data audits verifying the integrity and accuracy of inputs
B. The measures in place to ensure the appropriateness and relevance of input data for generative AI models
C. The algorithms for generative AI models designed to detect and correct data bias before processing
D. The effectiveness of data anonymization processes that help preserve data quality

Answer: B

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 AAIA™ 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.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI"

NEW QUESTION # 124
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?

A. Document sources and data processes.
B. Create dashboards to show outputs.
C. Use tools that explain model decisions.
D. Provide periodic model audit reports.

Answer: C

Explanation:
Transparency in AI, especially in regulated sectors like finance, is best achieved by using explainability tools that interpret the internal workings and outputs of complex ML models. The AAIATM Study Guide highlights model explainability as essential for both auditors and regulators to understand why certain decisions are made.
"Explainability tools allow auditors to trace predictions back to input features, helping verify fairness, logic, and compliance. These tools support transparency and trustworthiness in black- box models."

NEW QUESTION # 125
Which of the following would provide the BEST evidence to an IS auditor that an AI model's outputs are effectively controlled for bias?

A. The organization's AI policies include a clear definition of fairness.
B. Model training is restricted to data containing real-world human decisions.
C. Accuracy ranges for various demographic groups are similar.
D. Technical details of model development processes are transparent.

Answer: C

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 # 126
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