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MLA-C01 Practice Question: A data scientist is preparing a dataset for a…
A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?
⚠ Common exam trap
MLA-C01 often tests data leakage from resampling before the train/test split — candidates pick 'apply SMOTE to the whole dataset' thinking more data is always better.
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
✓
Oversample the minority class in the training set using SMOTE, and keep the test set as is.
With a 2% positive class, the training set needs class balancing to help the model learn the minority signal, but the hold-out test set must reflect the true 2% prevalence so evaluation metrics (precision, recall, PR-AUC) are realistic. Applying SMOTE only to the training set and leaving the test set untouched satisfies both requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Undersample the majority class in the training set to match the minority class size.
Why it's wrong here
Undersampling discards a large number of samples, potentially losing valuable information and harming model performance.
- ✓
Oversample the minority class in the training set using SMOTE, and keep the test set as is.
Why this is correct
SMOTE on the training set only addresses class imbalance during training; the test set preserves the original distribution for a realistic assessment.
- ✗
Randomly oversample the minority class in the entire dataset and then split.
Why it's wrong here
Random oversampling before splitting can cause the same original samples to appear in both training and test sets, leading to data leakage.
- ✗
Apply SMOTE to the entire dataset before splitting into training and test sets.
Why it's wrong here
Applying SMOTE before splitting would cause synthetic samples to leak into the test set, leading to an unrealistic evaluation.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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