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 using a large dataset with class imbalance (90% negative, 10% positive). After training a logistic regression model, the F1 score is low but accuracy is high. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy.
Option A (resample training data) and Option C (use different evaluation metric) are correct because class imbalance causes the model to be biased toward the majority class, leading to high accuracy but poor F1. Resampling (e.g., SMOTE) balances classes, and using F1 or AUC-ROC focuses on minority class performance. Option B (feature scaling) is a general preprocessing step but doesn't directly address imbalance. Option D (increase regularization) might reduce overfitting but doesn't target imbalance. Option E (add more features) may not help if the model is already biased.
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.
✓
Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy.
Why this is correct
Correct: Metrics like F1 are robust to class imbalance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply feature scaling to ensure all features contribute equally.
Why it's wrong here
Incorrect: Feature scaling is beneficial but not specific to class imbalance.
✗
Add more features to the model to improve its capacity.
Why it's wrong here
Incorrect: Adding features without addressing imbalance may not help.
✓
Resample the training data using techniques like SMOTE to balance the classes.
Why this is correct
Correct: Resampling addresses class imbalance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the regularization parameter to reduce overfitting.
Why it's wrong here
Incorrect: Regularization doesn't directly address class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Switch to evaluation metrics such as F1 score or AUC-ROC instead of accuracy. — Option A (resample training data) and Option C (use different evaluation metric) are correct because class imbalance causes the model to be biased toward the majority class, leading to high accuracy but poor F1. Resampling (e.g., SMOTE) balances classes, and using F1 or AUC-ROC focuses on minority class performance. Option B (feature scaling) is a general preprocessing step but doesn't directly address imbalance. Option D (increase regularization) might reduce overfitting but doesn't target imbalance. Option E (add more features) may not help if the model is already biased.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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