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| Section | Objectives |
|---|---|
| AI Governance and Risk | - AI Security and Controls
|
| AI Auditing Tools and Techniques | - AI Audit Planning
|
| AI Operations | - AI Data Management
|
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NEW QUESTION # 165
Which of the following would be the MOST effective way to test whether a deployed model's performance has degraded due to seasonal changes in customer behavior?
Answer: A
Explanation:
Seasonal or cyclical shifts in behavior require continuous, periodic monitoring against fresh data to detect performance degradation that a single point-in-time validation would miss.
NEW QUESTION # 166
Which of the following AI documents would support an IS auditor assessing hyperparameter tuning records?
Answer: C
Explanation:
Hyperparameter tuning involves adjusting settings like learning rate, batch size, or layer depth to optimize performance. This process is documented in " Model development logs, " which record the different " experiments " run by data scientists, the parameters used in each, and the resulting performance metrics (e.g., accuracy or loss). For an auditor, these logs are the " Audit Trail " of the model ' s construction. Data sheets (Option A) provide high-level info on datasets, and explainability reports (Option C) focus on outputs, but development logs provide the technical history of how the model was " tuned " to its final state.
NEW QUESTION # 167
An IS auditor uses an AI model to summarize worksheets, but several worksheets contained a
"Hidden Cell" instructing the model to ignore control failures. Which solution BEST mitigates the risk?
Answer: B
Explanation:
This is a "Data-based Prompt Injection," where the AI treats data as instructions. By "Allowing extraction only to predefined values and headers," the auditor restricts the AI's "Context Window" to specific, known data fields. This prevents the AI from "reading" and executing malicious commands hidden in other cells.
NEW QUESTION # 168
To confirm the fairness of AI model decisions, the BEST way to collect reliable evidence during an AI audit is by:
Answer: C
Explanation:
Testing the AI model with a curated and representative sample data set allows auditors to directly evaluate the fairness and bias of model decisions. This approach is aligned with best practices outlined in the AAIA™ Study Guide, as it enables quantifiable analysis of model behavior across different demographics or input scenarios.
"To assess fairness, auditors should use controlled data sets to evaluate whether model outputs disproportionately impact specific groups. This empirical testing provides stronger evidence than qualitative methods." While metadata (A) and developer interviews (C) can supplement findings, only B provides objective, reproducible evidence. Option D may reflect real-world interactions but lacks the control and consistency required in an audit.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Fairness and Bias Testing in AI Systems"
NEW QUESTION # 169
An internal audit department notices that AI-generated audit reports are producing false conclusions. Which of the following is the BEST way to correct this issue?
Answer: B
Explanation:
If AI-generated audit reports containfalse conclusions, the integrity of audit evidence and overall assurance is compromised. According to AAIA, when AI systems compromiseaccuracy of audit outputs, the immediate step should be tosuspend use(B) to prevent further incorrect or misleading reporting.
This allows auditors to:
* Investigate the root cause of incorrect conclusions
* Validate the model's training data, rules, and prompt structures
* Reassess controls, safeguards, and human review processes
* Prevent reliance on unreliable AI-generated audit material
Increasing context (A) or reducing creativity (C) may help generative models but do not guarantee correction of fundamental errors. Updating SLAs (D) is administrative and does not solve the immediate integrity issue.
References:
AAIA Domain 3: Audit Evidence, Professional Skepticism, and AI output validation.
AAIA Domain 2: Monitoring AI Outputs for Accuracy.
NEW QUESTION # 170
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