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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 450 (scaled, range 200–800) |
| Exam Duration: | 150 minutes |
| Certificate Validity Period: | 3 years |
| Exam Format: | Scenario-based questions, Computer-based testing, Multiple-choice questions |
| Related Certifications: | CISA CPA ACCA CIA FCCA |
| Real Exam Qty: | 90 |
| Available Languages: | Spanish, Chinese, English |
| Exam Price: | US$459 (member) / US$599 (non-member) |
| Recommended Training: | AAIA Official Review Course AAIA Review Manual |
| 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 # 265
Which of the following is the BEST key performance indicator (KPI) to measure a model ' s performance on imbalanced datasets?
Answer: C
Explanation:
For imbalanced datasets, standard " accuracy " or " error rate " is misleading (e.g., if 99% of transactions are legitimate, a model that predicts " legitimate " every time is 99% accurate but useless for fraud detection). The
" F1 score " is the harmonic mean of Precision and Recall, providing a balanced metric that considers both false positives and false negatives. It is the industry-standard KPI for evaluating how well a model handles the minority class. While Precision and Recall are valuable individually, the F1 score provides a single, comprehensive measure of the model ' s effectiveness in skewed environments.
NEW QUESTION # 266
Which technique is MOST useful for identifying whether a model's outputs disproportionately disadvantage a protected group?
Answer: A
Explanation:
Fairness metrics (e.g., demographic parity, equalized odds) are specifically designed to compare outcome rates across subgroups, revealing disparate impact that a generic accuracy metric would mask.
NEW QUESTION # 267
Which of the following AI system characteristics would BEST help an IS auditor evaluate the system's algorithm?
Answer: D
Explanation:
Transparency is a foundational principle for AI governance and effective risk management. When evaluating AI systems, IS auditors must assess not just how an algorithm functions, but also whether its processes and decisions are understandable and explainable. According to the ISACA Advanced in AI AuditTM (AAIATM) Study Guide, "algorithmic transparency--enabling auditors and stakeholders to see and understand the rationale behind automated decisions--is essential for ensuring accountability, fairness, and compliance with both organizational policies and regulatory requirements." Transparent justification of decisions (option C) directly supports an auditor's ability to evaluate the appropriateness, logic, and fairness of the AI's outputs. This is critical in identifying bias, ensuring ethical considerations are met, and verifying that outcomes are consistent with intended objectives. Without transparent justification, it is difficult for an IS auditor to independently assess whether decisions are being made based on valid, legal, and ethical grounds.
NEW QUESTION # 268
An organization has introduced an AI chat system where customers can enter their preferences and the system returns the best product selections. Which of the following is the BEST way to mitigate the risk of the system providing suggestions that may upset customers?
Answer: C
Explanation:
The risk described is that customer-facing suggestions may be inappropriate, insensitive, or offensive. The BEST mitigation is to perform testing of diverse scenarios (B)--including edge cases, demographic variations, and sensitive contexts--to confirm that outputs remain within acceptable business, ethical, and customer-experience thresholds. AAIA highlights scenario- based testing and fairness/impact assessments as key practices, especially where recommendations directly influence customer interactions.
NEW QUESTION # 269
An IS auditor is planning an audit of an AI medical prognosis system. Which of the following is the BEST way to test model transparency?
Answer: D
Explanation:
SHAP (Shapley Additive Explanations) is the leading, industry-recognized method for explaining complex AI model predictions.
AAIA specifically identifies SHAP as a robust technique for transparency in high-impact systems such as medical AI.
SHAP provides:
Feature contribution breakdowns
Visual explanation of each decision
Global and local interpretability
Quantifiable insights into model logic
Compliance-friendly justification of predictions
NEW QUESTION # 270
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