AI0-001 AI Concepts and Foundations Practice Question
A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?
⚠ Common exam trap
CompTIA often tests the misconception that accuracy is always the best metric, but in imbalanced datasets it is misleading, and candidates must recognize that F1-score (or precision-recall curves) is the correct choice for rare positive classes.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1-score
When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
True negative rate
Why it's wrong here
TNR is high for a majority-negative model, not informative.
- ✓
F1-score
Why this is correct
Correct; F1 considers both precision and recall.
- ✗
Mean squared error
Why it's wrong here
MSE is used for regression tasks.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading with class imbalance.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.