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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Related Certifications: | FCCA CIA CPA CISA ACCA |
| Exam Duration: | 120 minutes |
| Passing Score: | 65% |
| Available Languages: | English |
| Exam Format: | Remotely Proctored, Computer-Based, Multiple Choice |
| Real Exam Qty: | 90 |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Online remotely proctored computer-based exam |
| Pre Condition: | Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
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NEW QUESTION # 276
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?
Answer: A
Explanation:
If the combined size of the training, validation, and testing sets exceeds the original data size, it suggests that records may have been reused across sets. This can lead to data leakage, where the model has access to test or validation information during training, resulting in overly optimistic performance metrics.
"Data leakage invalidates model evaluation because it introduces unintended data overlap.
Auditors must ensure that the training, validation, and test sets are strictly partitioned."
NEW QUESTION # 277
Which of the following is the BEST key performance indicator (KPI) to measure a model's performance on imbalanced datasets?
Answer: A
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 # 278
An IS auditor reviews an AI tool using K-means to cluster customers. One cluster shows very high spending but low product diversity. What should the auditor recommend?
Answer: B
Explanation:
K-means clustering is an unsupervised learning technique that groups data based on similarity.
Discovering a cluster with high spending but low product diversity is a plausible and meaningful business insight: it may represent loyal customers who repeatedly purchase a narrow range of products. The auditor should therefore recommend treating this cluster as a potentially valid segment (B), subject to further business analysis and controls where appropriate.
NEW QUESTION # 279
Which of the following is the BEST recommendation for an organization that has adopted "vibe coding" (using AI to generate code based on high-level natural language prompts)?
Answer: D
Explanation:
"Vibe coding" or AI-assisted development carries the risk of introducing "hallucinated" vulnerabilities or insecure coding patterns that the AI learned from public (and potentially flawed) repositories. The most responsible control is to implement a rigorous, human-led "security checklist" for code reviews. This ensures that every line of AI-generated code is checked for common flaws like SQL injection or hardcoded credentials.
NEW QUESTION # 280
Which of the following is MOST important to review in order to gain assurance that an AI model is performing without biases?
Answer: A
Explanation:
Bias in AI models is most commonly introduced through the training data. The AAIATM Study Guide highlights that to ensure fairness, auditors and developers must evaluate the diversity, representativeness, and quality of the data used to train the model.
"The greatest source of bias in AI comes from the training data. Reviewing and auditing this data is critical to ensuring that outputs do not disproportionately affect specific groups or skew results."
NEW QUESTION # 281
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