Question 604 of 1,672
MLS-C01 Data Leakage Practice Question
Which THREE statements about data leakage in machine learning are correct? (Select THREE.)
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
✓
Using the target variable to filter features before splitting leads to data leakage
Using the target variable to filter features before splitting allows test set information to influence feature selection, causing data leakage. Option B is correct: applying SMOTE after splitting the dataset into training and test sets prevents leakage that would occur if SMOTE were applied before splitting, as synthetic samples would then be generated using information from the entire dataset. While SMOTE after splitting does not prevent all forms of leakage, the statement 'prevents data leakage' is interpreted in the context of the specific leakage that SMOTE can introduce, making it a correct practice. Option C is correct: standardizing the entire dataset before splitting uses statistics computed from both training and test data, which leaks information about the test set into the training process. Option D is incorrect because cross-validation does not eliminate all leakage; if preprocessing steps like scaling are applied to the entire dataset before cross-validation, leakage still occurs. Option E is incorrect because for time series data, a random train-test split ignores the temporal order and can cause future information to leak into past predictions; a time-based split is recommended.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using the target variable to filter features before splitting leads to data leakage
Why this is correct
Correct. Filtering features based on the target before splitting uses test set information to decide which features to keep, causing data leakage.
- ✓
Applying SMOTE after splitting the dataset prevents data leakage
Why this is correct
Correct. SMOTE should be applied after splitting to ensure that synthetic samples are generated only from training data, preventing leakage from test data into the oversampling process.
- ✓
Applying standardization on the entire dataset before splitting into training and test sets can cause data leakage
Why this is correct
Correct. Standardization using parameters computed from the entire dataset leaks information from the test set into the training set, leading to overoptimistic performance estimates.
- ✗
Using cross-validation eliminates all possible data leakage
Why it's wrong here
Incorrect. Cross-validation does not eliminate all data leakage; for instance, if feature selection or scaling is performed before the cross-validation loop, leakage still occurs.
- ✗
For time series data, using a random train-test split is recommended to avoid data leakage
Why it's wrong here
Incorrect. For time series data, a random split ignores temporal dependencies and can cause leakage where future data influences training; a time-based split is required.
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Last reviewed: Jun 20, 2026
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