This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model is evaluated using accuracy, but the accuracy is 99% even though the model predicts all instances as negative. Which metric should the data scientist use to properly evaluate the model?
Root mean squared error (RMSE)
Why wrong: RMSE is for regression problems, not classification.
Mean squared error (MSE)
Why wrong: MSE is for regression problems, not classification.
F1 score
F1 score combines precision and recall, providing a better measure for imbalanced classification.
Accuracy
Why wrong: Accuracy is misleading for imbalanced datasets because a model that always predicts the majority class can achieve high accuracy.
Answer choices
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 the harmonic mean of precision and recall, making it robust to class imbalance. With 99% negative instances, accuracy is misleadingly high even if the model never predicts the positive class. F1 captures both false positives and false negatives, providing a balanced evaluation of the minority class performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Root mean squared error (RMSE)
Why it's wrong here
RMSE is for regression problems, not classification.
Mean squared error (MSE)
Why it's wrong here
MSE is for regression problems, not classification.
F1 score
Why this is correct
F1 score combines precision and recall, providing a better measure for imbalanced classification.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets because a model that always predicts the majority class can achieve high accuracy.
Common exam traps
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is unreliable for imbalanced datasets, and they may incorrectly choose accuracy or a regression metric without considering the need for a precision-recall based metric like F1.
Detailed technical explanation
The F1 score is computed as 2 * (precision * recall) / (precision + recall), where precision = TP/(TP+FP) and recall = TP/(TP+FN). In imbalanced scenarios, the F1 score can be macro-averaged (unweighted mean per class) or weighted by support to handle multi-class settings. A real-world example is fraud detection, where positive cases are rare (<1%) and a model with high accuracy but zero recall would miss all fraudulent transactions, incurring significant financial losses.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: F1 score — The F1 score is the harmonic mean of precision and recall, making it robust to class imbalance. With 99% negative instances, accuracy is misleadingly high even if the model never predicts the positive class. F1 captures both false positives and false negatives, providing a balanced evaluation of the minority class performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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