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  4. ISACA.AAIA.v2026-06-21.q153 Dumps
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Question 151

Which metric should an IS auditor review to evaluate issues with data collection that could impact AI model training?

Correct Answer: B
The percentage of missing values (option B) directly reflects data collection issues. Missing or incomplete data can degrade model performance, distort feature distributions, and create biased or inaccurate predictions.
AAIA stresses that auditors must evaluate:
* Completeness
* Validity
* Accuracy
* Consistency
Missing values signal failures in upstream processes, including sensors, user inputs, integrations, or data pipelines.
The other metrics are unrelated to raw data integrity:
* Epochs (A) refer to training cycles.
* Percentage of training data (C) concerns dataset partitioning, not quality.
* True positives (D) relate to model performance, not data collection quality.
References:
AAIA Domain 2: Data Quality, Completeness, and Integrity
AAIA Domain 3: Pre-Training Data Validation
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Question 152

An organization wants to evaluate whether its facial recognition system performs equally well across different skin tones. Which testing approach is MOST appropriate?

Correct Answer: B
Disaggregated testing evaluates model accuracy separately for each subgroup, revealing performance gaps that aggregate metrics would conceal -- a well-known concern in facial recognition systems.
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Question 153

An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

Correct Answer: D
Adversarial attacks involve "manipulated inputs" (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must "Validate the process for handling manipulated inputs." This includes checking for robust input validation, anomaly detection on incoming data, and "adversarial training" where the model is intentionally exposed to these attacks during development to build resilience.
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