Question 898 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?
⚠ Common exam trap
AWS often tests whether candidates confuse data preprocessing techniques (scaling, encoding, dimensionality reduction) with methods that directly modify the class distribution, leading them to pick a plausible but irrelevant option like PCA or scaling.
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
✓
Random oversampling of the minority class
Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA) dimensionality reduction
Why it's wrong here
PCA reduces feature dimensions but does not address class imbalance.
- ✓
Random oversampling of the minority class
Why this is correct
Random oversampling is a valid technique to balance classes by replicating minority samples.
- ✗
Standard scaling of numerical features
Why it's wrong here
Scaling normalizes feature ranges but does not change class proportions.
- ✗
One-hot encoding of categorical variables
Why it's wrong here
One-hot encoding transforms categorical data but does not affect class balance.
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Last reviewed: Jun 30, 2026
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