MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

⚠ Common exam trap

AWS often tests the distinction between data splitting techniques and model evaluation methods, so the trap here is that candidates confuse k-fold cross-validation (a validation strategy) with a static split technique, leading them to select option A.

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

✓

Stratified split

Stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.

Answer analysis

Option-by-option breakdown

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

✗
K-fold cross-validation split
Why it's wrong here
K-fold is a cross-validation technique, not a single train/validation/test split.
✗
Chronological split
Why it's wrong here
Chronological split is for time-series data, not categorical targets.
✓
Stratified split
Why this is correct
Stratified split ensures each subset has the same class distribution as the original dataset.
✗
Random split
Why it's wrong here
Random split may not maintain class proportions across splits.

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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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