MLS-C01 Modeling Practice Question
A company is building a model to classify customer reviews as positive or negative. The dataset has 10,000 positive and 100 negative reviews. Which metric is most appropriate for evaluating model performance?
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.
(F1 score) is most appropriate because it balances precision and recall, making it suitable for imbalanced datasets where accuracy can be misleading (e.g., a model predicting all positive would achieve 99% accuracy). Option B (Accuracy) is misleading in imbalanced scenarios. Option C (Mean squared error) is for regression, not classification. Option D (AUC-ROC) can be used but may provide an overly optimistic assessment in highly imbalanced data.
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 considers both precision and recall, good for imbalance.
- ✗
Accuracy.
Why it's wrong here
Accuracy is not suitable for imbalanced classes.
- ✗
Mean squared error.
Why it's wrong here
MSE is for regression, not classification.
- ✗
AUC-ROC.
Why it's wrong here
AUC-ROC may be too optimistic for severe imbalance.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 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 →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.