MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?
⚠ Common exam trap
AWS often tests the distinction between data-level techniques (like SMOTE, oversampling, undersampling) and algorithm-level techniques (like class weights), and the trap here is that candidates confuse class weighting as a data preparation method when it is actually a model training adjustment, not a data transformation step.
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
✓
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SMOTE (Synthetic Minority Over-sampling Technique)
Why this is correct
Generates synthetic samples for the minority class.
- ✗
Random undersampling of the majority class
Why it's wrong here
Removes potentially valuable data.
- ✗
Random oversampling of the minority class
Why it's wrong here
Duplicates existing samples, risk of overfitting.
- ✗
Apply class weights during model training
Why it's wrong here
Affects loss function, not data preparation.
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