MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
A financial services company is developing a fraud detection model using Amazon SageMaker. They have a dataset with 10 million transactions, each with 300 features. The dataset is highly imbalanced (0.1% fraud). They have performed feature engineering and now need to split the data for training, validation, and test sets. The data is stored in CSV files in Amazon S3. They plan to use SageMaker's built-in XGBoost algorithm. To ensure proper evaluation and avoid data leakage, which data splitting strategy should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Perform a stratified split on the target variable to ensure each set has the same fraud ratio.
Option C is correct because a stratified split preserves the original 0.1% fraud ratio across training, validation, and test sets, which is critical for imbalanced datasets. This ensures each subset is representative of the population, allowing SageMaker's XGBoost to be evaluated fairly without data leakage. Random splits (Option A) could accidentally create a validation or test set with zero fraud cases, making evaluation meaningless.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Randomly shuffle the entire dataset and then split into 80% training, 10% validation, 10% test.
Why it's wrong here
Random splitting may produce validation/test sets with extremely few fraud cases.
✗
Use k-fold cross-validation on the entire dataset and average the results.
Why it's wrong here
Cross-validation without stratification can still have imbalance issues in folds.
✓
Perform a stratified split on the target variable to ensure each set has the same fraud ratio.
Why this is correct
Stratified splitting preserves class proportions, enabling reliable evaluation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply SMOTE to balance the dataset first, then split randomly into training, validation, and test sets.
Why it's wrong here
Synthetic data must not appear in test set; this leaks information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose random splitting (Option A) out of habit, forgetting that imbalanced datasets require stratified sampling to avoid evaluation sets with zero positive cases, which would render metrics like precision and recall undefined.
Detailed technical explanation
How to think about this question
Stratified splitting uses the target variable to ensure each fold maintains the class distribution of the original dataset. In SageMaker, you can implement this by using `train_test_split` with the `stratify` parameter from scikit-learn before uploading to S3, or by using SageMaker's built-in `S3DataSource` with a custom splitting script. For extreme imbalance (0.1%), even stratified splits may need multiple folds or repeated splits to ensure minority class representation in all subsets.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Perform a stratified split on the target variable to ensure each set has the same fraud ratio. — Option C is correct because a stratified split preserves the original 0.1% fraud ratio across training, validation, and test sets, which is critical for imbalanced datasets. This ensures each subset is representative of the population, allowing SageMaker's XGBoost to be evaluated fairly without data leakage. Random splits (Option A) could accidentally create a validation or test set with zero fraud cases, making evaluation meaningless.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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