AI0-001 AI Concepts and Techniques Practice Question
A machine learning team is evaluating a logistic regression model for a binary classification task. The dataset has 1,000 samples and 20 features. Which TWO metrics are most appropriate for evaluating model performance? (Choose 2)
⚠ Common exam trap
CompTIA AI often tests the distinction between metrics for classification versus metrics for sequence generation or language modeling, leading candidates to mistakenly select BLEU or perplexity for a binary classification task.
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
The F1 score is appropriate because it balances precision and recall, making it robust for binary classification when class distribution may be imbalanced. AUC-ROC measures the model's ability to distinguish between positive and negative classes across all classification thresholds, providing a threshold-independent evaluation of discriminative performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BLEU score
Why it's wrong here
BLEU is for evaluating machine translation outputs.
- ✓
F1 score
Why this is correct
F1 score combines precision and recall, useful when class distribution is uneven.
- ✗
Perplexity
Why it's wrong here
Perplexity is used for language models, not binary classifiers.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading with imbalanced classes; not always the best metric.
- ✓
AUC-ROC
Why this is correct
AUC-ROC provides a threshold-independent measure of classification performance.
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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.