- A
F1 score
F1 score combines precision and recall, making it suitable for imbalanced datasets where both false positives and false negatives are important.
- B
AUC-ROC
Why wrong: AUC-ROC may be overly optimistic on imbalanced datasets because it evaluates performance across all thresholds, often overemphasizing the majority class.
- C
Precision
Why wrong: Precision alone does not consider recall; a model could have high precision but low recall, missing many positive cases.
- D
Accuracy
Why wrong: Accuracy can be misleading when the positive class is rare because a model that always predicts the majority class can achieve high accuracy.
Question 135 of 1,755
MLS-C01 Modeling Practice Question
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 classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve 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
The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 combines precision and recall, making it suitable for imbalanced datasets where both false positives and false negatives are important.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC may be overly optimistic on imbalanced datasets because it evaluates performance across all thresholds, often overemphasizing the majority class.
- ✗
Precision
Why it's wrong here
Precision alone does not consider recall; a model could have high precision but low recall, missing many positive cases.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading when the positive class is rare because a model that always predicts the majority class can achieve high accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the misconception that accuracy is always the best metric, but the trap here is that on imbalanced datasets, accuracy is misleadingly high even when the model fails to detect the rare positive class, so candidates must recognize that F1 score (or precision-recall AUC) is the appropriate choice.
Detailed technical explanation
How to think about this question
The F1 score ranges from 0 to 1 and is defined as 2 * (precision * recall) / (precision + recall). In imbalanced classification, threshold tuning (e.g., lowering the decision threshold from 0.5 to 0.3) can improve recall at the cost of precision, and the F1 score captures the net effect. Real-world scenarios like fraud detection or rare disease diagnosis rely on F1 because missing a positive (low recall) is often more harmful than a false alarm.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F1 score — The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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