- A
Using the target variable to filter features before splitting leads to data leakage
Correct. Filtering features based on the target before splitting uses test set information to decide which features to keep, causing data leakage.
- B
Applying SMOTE after splitting the dataset prevents data leakage
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.
- C
Applying standardization on the entire dataset before splitting into training and test sets can cause data leakage
Correct. Standardization using parameters computed from the entire dataset leaks information from the test set into the training set, leading to overoptimistic performance estimates.
- D
Using cross-validation eliminates all possible data leakage
Why wrong: 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.
- E
For time series data, using a random train-test split is recommended to avoid data leakage
Why wrong: 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.
MLS-C01 Data Leakage Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: data Leakage. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO statements about data leakage in machine learning are correct? (Select TWO.)
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
Option A is correct because 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.
Key principle: Data Leakage
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.
Related concept
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.
Related concept
Data Leakage
- ✓
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.
Related concept
Data Leakage
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Data Leakage
- SMOTE
- Preprocessing Leakage
- Temporal Leakage
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Data Leakage
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review data Leakage, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Data Leakage.
What is the correct answer to this question?
The correct answer is: Using the target variable to filter features before splitting leads to data leakage — Option A is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Review data Leakage, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Data Leakage
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Last reviewed: Jun 20, 2026
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