AI0-001 AI Concepts and Techniques Practice Question
A data scientist is evaluating a binary classification model. The model achieves 95% accuracy on the test set, but the precision is 0.60 and recall is 0.55. The dataset has 90% negative class samples. Which metric should the team focus on to improve the model?
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
With high class imbalance (90% negatives), accuracy is misleading. F1 score balances precision and recall, giving a better picture of performance on the minority class. AUC-ROC is also good but F1 directly optimizes for positive class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, providing a balanced metric that accounts for both false positives and false negatives.
- ✗
Perplexity
Why it's wrong here
Perplexity is used for language models, not binary classification.
- ✗
BLEU score
Why it's wrong here
BLEU is for evaluating machine translation, not classification.
- ✗
Accuracy
Why it's wrong here
Accuracy is inflated by the majority class; improving accuracy may not help the minority class.
About these practice questions
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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.