Question 1,282 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). Which TWO techniques should be used to address the class imbalance? (Choose 2.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that regularization or dimensionality reduction can fix class imbalance, but these techniques address overfitting or computational efficiency, not skewed class distributions.
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
✓
Use random undersampling of the majority class
Random undersampling of the majority class (Option C) reduces the number of non-churner samples to balance the dataset, preventing the model from being biased toward the majority class. SMOTE (Option D) generates synthetic samples for the minority class by interpolating between existing minority instances, which increases the representation of churners without simply duplicating data. Both techniques directly address class imbalance by modifying the training data distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a higher learning rate during training
Why it's wrong here
Learning rate does not fix imbalance.
- ✗
Use L1 regularization on the model
Why it's wrong here
L1 regularization helps feature selection, not imbalance.
- ✓
Use random undersampling of the majority class
Why this is correct
Undersampling reduces majority class samples, balancing the dataset.
- ✓
Use SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic minority samples, reducing imbalance.
- ✗
Use principal component analysis (PCA) to reduce dimensionality
Why it's wrong here
PCA does not address class imbalance.
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Last reviewed: Jul 4, 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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