AAIA Exam Dumps | Which metric should an IS auditor review to evaluate issues with data collection that could impact AI

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Question 33/111

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

A. Percentage of epochs used

B. Percentage of missing values

C. Percentage of data in training dataset

D. Percentage of true positives on confusion matrix

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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