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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Real Exam Qty: | 90 |
| Passing Score: | 450 (scaled, range 200–800) |
| Related Certifications: | CIA CISA FCCA CPA ACCA |
| Exam Price: | US$459 (member) / US$599 (non-member) |
| Exam Duration: | 150 minutes |
| Certificate Validity Period: | 3 years |
| Available Languages: | Spanish, Chinese, English |
| Exam Format: | Multiple-choice questions, Computer-based testing, Scenario-based questions |
| Recommended Training: | AAIA Review Manual AAIA Official Review Course |
| Exam Registration: | ISACA Official Registration |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Computer-based; PSI test centers or remote proctored (remote not available in India, Mainland China, Hong Kong) |
| Pre Condition: | Hold active CISA, CIA, CPA, ACCA, FCCA or equivalent qualified certification with IT audit/advisory focus |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia/aaia-exam-content-outline |
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NEW QUESTION # 213
A retailer's AI pricing engine recommends unusually large discounts in certain regions. An audit reveals that most historical sales data used for model training came from large urban areas, while smaller regions were represented by very few or no sales records. Which of the following is the MOST likely cause of this issue?
Answer: D
Explanation:
This scenario illustrates the problem of "Data Scarcity" or "Under-representation bias." If the model is trained primarily on urban data, it will not have enough information to understand the unique economic or competitive dynamics of rural regions. As a result, when it encounters those regions, it may make erratic or extreme recommendations (like excessive discounts) because it is essentially "guessing" based on limited data. According to the AAIATM manual, ensuring "Data Representativeness" across all demographic or geographic subgroups is a critical step in training.
Without a balanced dataset, the model fails to generalize correctly to all operational environments, leading to significant financial loss and operational instability.
NEW QUESTION # 214
An IS auditor is reviewing change documentation of an AI model. Which of the following would pose the GREATEST risk?
Answer: C
Explanation:
Alow number of test scenarios(C) indicates insufficient testing coverage, creating a high likelihood that errors, biases, or edge-case failures remain undetected. AI systems requirebroad, diverse scenario testing, including fairness, robustness, and stress testing. AAIA highlights comprehensive testing as essential to AI reliability and risk mitigation.
Lack of standardized test formats (B) is inefficient but less severe. Missing seed documentation (D) affects reproducibility but not completeness. Lack of management testing (A) matters, but insufficient test scenarios pose thelargest direct riskto model safety and performance.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Testing Techniques for AI Solutions.
NEW QUESTION # 215
An IS auditor is testing an AI model used for determining insurance premiums and eligibility. Which of the following is the MOST effective testing method to identify bias in algorithm outputs?
Answer: D
Explanation:
Disparate impact analysisis a specific technique used to detect whether model decisions disproportionately disadvantage certain protected or sensitive groups (e.g., by gender, age, ethnicity, or other attributes). For an AI model determiningpremiums and eligibility, fairness and non-discrimination are critical regulatory and ethical requirements, and AAIA content highlights fairness and bias evaluation as core elements of AI governance and risk management.
Regression testing (A) checks that changes do not introduce defects in previously functioning components, not fairness. Cross-cluster analysis (B) may reveal patterns but is not inherently a fairness test. Predictive analytics (D) is a broad term for forward-looking analysis, not a method specifically designed to detect bias.
Therefore,disparate impact analysisis the most appropriate and targeted method to identify bias in the insurance AI model's outputs.
References:
ISACA,AAIA Exam Content Outline- Domain 1: AI Governance and Risk (bias, fairness, and transparency considerations).
ISACA AI ethics and fairness guidance used in AAIA training materials.
NEW QUESTION # 216
Which of the following is the GREATEST concern when an audit team relies on generative AI to create audit reports?
Answer: A
Explanation:
The greatest concern is that the generative model may hallucinate, producing incorrect facts or conclusions (option B). In an audit context, hallucinations can create false statements about control effectiveness, misreport risks, or incorrectly summarize evidence.
AAIA stresses that auditors must maintain professional skepticism and validate AI-generated content. Misstatements are high-risk because they undermine audit credibility, regulatory compliance, and organizational decision-making.
NEW QUESTION # 217
An organization is reviewing its existing data governance framework after implementing an AI-based document repository solution. Which of the following should be the PRIMARY consideration?
Answer: A
Explanation:
The PRIMARY consideration isdata classification(B), because the AI-based repository handles different types of documents that may contain sensitive, confidential, regulated, or public information. Proper classification ensures correct application of access controls, retention rules, encryption requirements, and privacy protections.
Retention (A) is important but flows from proper classification. Data enrichment (C) and qualitative data collection (D) are not foundational governance controls. AAIA emphasizesclassification as the first stepin robust AI data governance.
References:
ISACA,AAIA Exam Content Outline- Domain 1: Privacy and Data Governance Programs.
NEW QUESTION # 218
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