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
True negative rate measures correct rejection of negatives, which is trivially high when positives are only 1% and says nothing about detecting them. It is useful when the negative class is the one of interest, for example screening where false positives carry the cost.
- ✓
F1-score
Why this is correct
With a 1% positive rate, accuracy is misleading because predicting all negatives scores 99%. F1-score is the harmonic mean of precision and recall, so it penalises both missed positives and false alarms, giving a meaningful measure of minority-class performance.
- ✗
Mean squared error
Why it's wrong here
Mean squared error is a regression loss operating on continuous predictions, not a classification metric; on imbalanced labels it is dominated by the majority class. It would be the right choice when predicting a numeric target such as price or demand.
- ✗
Accuracy
Why it's wrong here
Accuracy measures the proportion of all predictions that are correct, so a classifier predicting the majority negative class for every instance still scores 99% when positives are 1% of the data, hiding the missed positives entirely. It is tempting because accuracy suits balanced datasets, where class frequencies are roughly equal and overall correctness reflects genuine performance.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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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.