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| Section | Objectives |
|---|---|
| AI Operations | |
| AI Auditing Tools and Techniques | |
| AI Governance and Risk |
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NEW QUESTION # 216
Which of the following would be MOST useful for an IS auditor when testing high-impact rare scenarios that have not yet occurred in a production environment?
Answer: B
Explanation:
For " edge cases " or " rare scenarios " (often called " black swan " events), historical data is insufficient because the events haven ' t happened yet. " Synthetic data " is artificially generated data that maintains the statistical properties of real data but includes simulated rare events. This allows auditors to " stress test " how the AI model would react to extreme conditions, such as a massive market crash or a rare medical emergency.
Anonymized or de-identified data only reflects past occurrences. Using synthetic data is a proactive testing strategy to ensure the AI ' s safety and robustness in unpredictable environments.
NEW QUESTION # 217
Which of the following is the MOST important course of action for an organization prior to allowing end users to utilize an AI tool?
Answer: B
Explanation:
An AI usage policy sets the foundation for safe, ethical, and effective AI deployment. According to the AAIATM Study Guide, having an AI policy in place ensures that users understand acceptable behaviors, limitations, and responsibilities when interacting with AI tools.
"AI acceptable use policies promote governance by clearly outlining the dos and don'ts of AI interaction, preventing misuse and aligning user activity with organizational values and compliance standards."
NEW QUESTION # 218
What should be the IS auditor's GREATEST concern when an organization is mixing AI- generated content into training data without labeling?
Answer: B
Explanation:
This is known as "Data Contamination" or "Model Autophagy". When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as "ground truth," which causes the model to
"Collapse" and lose its ability to accurately reflect the real world. The ISACA AAIATM manual emphasizes that training data must be "Primary" and "Authentic" to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model's logic.
NEW QUESTION # 219
The accuracy of an AI model used for product recommendations, trained on data from a specific year, is declining two years later as customer buying patterns have shifted toward sustainable products. Which of the following is the MOST likely cause?
Answer: B
Explanation:
Data drift (also known as model drift or concept drift) refers to the phenomenon where the statistical properties of the target variable or the input data change over time, leading to a decline in model performance.
In this scenario, the fundamental shift in customer preferences toward sustainability represents a change in the real-world environment that the model ' s original training data no longer reflects. As stated in the AAIA™ guidelines, models operating in dynamic environments like retail require continuous monitoring to detect when historical patterns are no longer predictive. Covariate shift (Option D) is a sub-type of drift specifically involving changes in the distribution of input features, but " Data drift " is the broader, most appropriate term for the observed performance degradation.
NEW QUESTION # 220
Which of the following is the PRIMARY reason IS auditors must be aware that generative AI may return different investment recommendations from the same set of data?
Answer: A
Explanation:
Generative AI systems, particularly those based on transformer models, produce outputs using probabilistic computations. As a result, even when given the same input data, these models may generate different outputs depending on sampling strategies (e.g., temperature, top-k sampling).
"Generative AI operates probabilistically, meaning that outputs can vary with each run based on stochastic sampling techniques. This variability is expected and must be accounted for in risk-sensitive environments like finance." While A and B refer to limitations and architecture, and D is unrelated to logic, C directly explains the output inconsistency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Stochastic Behavior in Generative Models"
NEW QUESTION # 221
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