Question 13
Domain 2 — AI Operations, Lifecycle, and Control EnvironmentWhen auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:
Correct answer: A
Explanation
Precision measures how many predicted positives are actually positive, so it reflects the rate of false positives. A model with high precision makes fewer false positive predictions, which is why false positives are best assessed by examining precision.
Why each option is right or wrong
A. Precision
Precision is defined as TP / (TP + FP), so the false-positive count is directly in the denominator and lowers the score when it rises. In an ML audit, a higher precision indicates fewer predicted positives are incorrect, making it the most direct metric for evaluating false positives.
B. Completeness
C. Accuracy
D. Recall