AI0-001 AI Concepts and Techniques Practice Question
An AI practitioner needs to measure the performance of a binary classification model for disease detection, where the cost of false negatives is very high. Which metric should be prioritized?
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
✓
Recall
Recall (true positive rate) minimises false negatives, which is critical when missing a positive case is dangerous.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, directly addressing the cost of false negatives.
- ✗
Precision
Why it's wrong here
Precision focuses on minimising false positives, not false negatives.
- ✗
F1-score
Why it's wrong here
F1-score balances precision and recall equally via their harmonic mean, but in this disease detection scenario the cost of false negatives is very high, so recall must be weighted more heavily than precision. F1-score treats both error types as equally important, failing to penalise missed positive cases sufficiently. It is tempting because F1-score is a standard metric for imbalanced classes, and would be correct when false positives and false negatives carry equal cost.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading if class imbalance exists; it does not specifically penalise false negatives.
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JA
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.