The answer is stratified random sampling, which is the correct method for splitting an imbalanced dataset because it preserves the original class distribution—95% negative and 5% positive—in both the training and test sets. This technique works by dividing the data into homogeneous subgroups (strata) based on the target class, then randomly sampling from each stratum proportionally, ensuring that the rare positive class is not underrepresented in either split. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of data leakage and proper preprocessing workflows; a common trap is confusing oversampling (which must be done after splitting) with the split method itself. Remember, stratified splitting maintains the imbalance for honest evaluation, while oversampling or SMOTE is applied only to the training set. Memory tip: “Stratify before you amplify”—always preserve class proportions in the split before any resampling.
MLS-C01 Exploratory Data Analysis 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. 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 data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use stratified random sampling to split the data
Option D is correct because stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because simple random sampling may result in uneven distribution. Option B is wrong because oversampling should be done after splitting to avoid data leakage. Option C is wrong because k-fold cross-validation is not a split method.
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.
✗
Use k-fold cross-validation and then split the data
Why it's wrong here
Cross-validation is a training technique, not a split method.
✗
Oversample the minority class first, then do a random split
Why it's wrong here
Oversampling before splitting can cause data leakage from test to training.
✗
Perform a simple random 80/20 split
Why it's wrong here
Simple random split may not preserve class distribution.
✓
Use stratified random sampling to split the data
Why this is correct
Stratified split preserves class proportions in each subset.
Related concept
Read the scenario before looking for a memorised answer.
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
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use stratified random sampling to split the data — Option D is correct because stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because simple random sampling may result in uneven distribution. Option B is wrong because oversampling should be done after splitting to avoid data leakage. Option C is wrong because k-fold cross-validation is not a split method.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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