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| Section | Objectives |
|---|---|
| AI Operations | |
| AI Auditing Tools and Techniques | |
| AI Governance and Risk |
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質問 # 21
Which of the following evaluation criteria is the HIGHEST priority when auditing an organization's management of AI vendors?
正解：A
解説：
When outsourcing AI, the organization retains accountability for the model's decisions. The highest priority for an auditor is ensuring "Interpretability"--the ability of the vendor to provide tools or documentation that explain the model's rationale. Without this, the organization cannot satisfy regulatory requirements for transparency (e.g., GDPR Right to Explanation) or verify that the vendor's model is not biased. While SLOs (Option B) are important for operational uptime, they do not mitigate the ethical or legal risks associated with "Black Box" algorithms provided by third parties.
質問 # 22
An organization developed an AI model trained on its monthly data. Which of the following would be the BEST validation method to avoid data drift?
正解：A
解説：
When dealing with monthly or temporal data, standard random splits (Option D) or cross- validation (Option A) can cause "temporal leakage," where the model inadvertently learns from future data to predict the past. According to ISACA AAIATM principles, "Time Series" validation is the most appropriate method for sequential data. It involves training the model on a specific period (e.g., months 1?0) and testing it on the subsequent period (e.g., month 11). This approach accurately reflects how the model will perform in production and is essential for detecting data drift, as it identifies when seasonal trends or long-term shifts in customer behavior cause the model's accuracy to degrade over time.
質問 # 23
An AI audit tool incorrectly flagged that business decisions were biased, leading to inappropriate management action plans. Which of the following can BEST prevent this risk?
正解：A
解説：
AI tools can produce "False Positives" in bias detection if they misinterpret the data. To prevent management from acting on incorrect audit findings, "Explainable AI (XAI) validation methods" (like SHAP or LIME) should be applied. XAI allows the auditor to see why the tool flagged a specific decision as biased. If the tool's reasoning is flawed (e.g., it ignored a valid business justification), the auditor can correct the finding before it reaches management. This adds a necessary layer of "Auditor skepticism" and human validation to AI-driven audit insights.
質問 # 24
An IS auditor is reviewing change management documentation of an AI model. Which of the following would pose the GREATEST risk to the model?
正解：D
解説：
In AI development, a "seed" ensures that random processes (like weight initialization) are reproducible. If an A/B test compares two models using different seeds, the auditor cannot tell if the performance difference is due to the model changes or simply due to "random luck" in how the weights were initialized. This invalidates the test results. For a fair "apple-to-apples" comparison, the seed should remain consistent. Tuning on a training set (Option B) is standard, though it risks overfitting; however, the lack of scientific control in testing (Option C) is a more immediate risk to the integrity of the change management process.
質問 # 25
An IS auditor is auditing an AI system that predicts inventory needs. The system recently failed to predict a stock outage for a key product. Which of the following audit tests would BEST validate the system's accuracy?
正解：A
解説：
The best way to validate the accuracy of a predictive AI system is to use historical testing with past sales data (option D). According to the AAIATM Study Guide, "historical (or back-testing) is essential for evaluating how well a model would have performed using actual data from previous periods, directly reflecting its predictive validity." This method reveals any gaps or biases in the model by comparing predictions to known outcomes.
Unit testing, load testing, and sensitivity analysis are useful for technical verification and robustness but do not provide direct evidence of prediction accuracy in real-world scenarios.
質問 # 26
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