Question 156 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)
⚠ Common exam trap
Candidates often confuse techniques for handling class imbalance with general data preprocessing or evaluation methods, leading them to select PCA or cross-validation as solutions, when in fact only resampling (SMOTE) and cost-sensitive learning (class weights) directly address the imbalance problem.
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
✓
Apply SMOTE only to training data
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the class distribution. Applying SMOTE only to the training data is critical to avoid data leakage, as the test set must remain untouched to provide an unbiased evaluation of model performance on the original class 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.
- ✗
Perform PCA to reduce dimensions
Why it's wrong here
PCA reduces features but does not address class imbalance.
- ✗
Remove features with low variance
Why it's wrong here
Low variance feature removal is a dimensionality reduction technique, not an imbalance solution.
- ✗
Use k-fold cross-validation
Why it's wrong here
Cross-validation is for model evaluation, not a direct treatment for imbalance.
- ✓
Apply SMOTE only to training data
Why this is correct
SMOTE generates synthetic minority samples, helping the model learn the minority class better.
- ✓
Use class weights in the algorithm
Why this is correct
Class weights penalize misclassifications of the minority class more heavily.
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Last reviewed: Jun 24, 2026
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