MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the imbalance before training?
⚠ Common exam trap
AWS often tests the misconception that any data preprocessing step (like normalization or cross-validation) can fix class imbalance, when in fact only resampling techniques (oversampling, undersampling, or synthetic generation) directly alter the class distribution.
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 oversample the positive class
SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize all features to a 0-1 range
Why it's wrong here
Normalization does not address class imbalance; it scales features.
- ✗
Use cross-validation to handle imbalance
Why it's wrong here
Cross-validation is a model evaluation technique, not a data preparation step for imbalance.
- ✗
Remove enough instances of the negative class to achieve balance
Why it's wrong here
Removing data leads to loss of potentially valuable information and is not recommended.
- ✓
Apply SMOTE to oversample the positive class
Why this is correct
SMOTE generates synthetic samples for the minority class, effectively balancing the dataset.
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