MLA-C01 Data Preparation for Machine Learning Practice Question

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

⚠ Common exam trap

AWS often tests the distinction between techniques that change the dataset distribution (like undersampling and oversampling) versus those that only affect model training or evaluation (like stratified splitting), leading candidates to mistakenly select stratified splitting as a balancing technique.

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

✓

Undersample the majority class

Undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Remove outliers from the minority class
Why it's wrong here
This would further reduce the minority class, worsening imbalance.
✗
Apply PCA to reduce dimensionality
Why it's wrong here
PCA does not address class imbalance.
✗
Use stratified splitting for train/test sets
Why it's wrong here
Stratified splitting maintains class proportions but does not balance the data itself.
✓
Undersample the majority class
Why this is correct
Reduces majority class size to balance with minority class.
✓
Oversample the minority class using SMOTE
Why this is correct
Generates synthetic samples for the minority class.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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