Question 849 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?
⚠ Common exam trap
AWS often tests the distinction between oversampling by duplication and synthetic oversampling (SMOTE), where candidates mistakenly choose simple duplication (Option A) because they think 'more data is always better,' failing to recognize that SMOTE generates diverse synthetic samples to reduce overfitting.
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 to generate synthetic samples for the minority class
SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Oversampling the minority class by duplicating examples
Why it's wrong here
Simple duplication can cause overfitting and does not introduce diversity.
- ✗
Collect more data to match the number of samples in both classes
Why it's wrong here
While collecting more data helps, it is not always practical and does not directly address the existing imbalance.
- ✗
Random undersampling of the majority class
Why it's wrong here
Undersampling discards potentially useful data and may lead to loss of information.
- ✓
Apply SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic examples along the line segments of minority class nearest neighbors, addressing imbalance.
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Last reviewed: Jun 30, 2026
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