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NEW QUESTION # 178
An organization outsources model development to a third party but retains responsibility for regulatory compliance. What contractual control is MOST important?
Answer: D
Explanation:
Because ultimate compliance accountability remains with the organization, contracts must grant audit rights and require transparency into methodology and testing so the organization can independently verify compliance.
NEW QUESTION # 179
Which metric should an IS auditor review to evaluate issues with data collection that could impact AI model training?
Answer: B
Explanation:
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.
NEW QUESTION # 180
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?
Answer: D
Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIATM manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations.
NEW QUESTION # 181
What should be the IS auditor's GREATEST concern when an organization is mixing AI- generated content into training data without labeling?
Answer: D
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 # 182
In deep learning neural networks, which of the following layers is considered MOST important for an IS auditor to evaluate because it performs feature extraction to mimic human decision- making?
Answer: C
Explanation:
In the context of Neural Networks, the "Hidden Layers" are where the actual transformation and feature extraction occur. These layers sit between the input and output, processing data through weighted connections to identify complex patterns. For an IS auditor, evaluating the hidden layers is critical because they represent the "logic" of the model that emulates human-like cognition.
While input and output layers are transparent, the hidden layers often lack interpretability, leading to risks of hidden bias or non-deterministic behavior. Understanding the depth and activation functions of these layers helps auditors assess the model's complexity and its susceptibility to errors.
NEW QUESTION # 183
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