MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.
Option C is correct because stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.
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 sample 10% of the data and plot feature distributions by class.
Why it's wrong here
Random sampling may miss the minority class entirely.
✗
Apply PCA to reduce dimensionality, then visualize the first two components.
Why it's wrong here
PCA does not address class imbalance.
✓
Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.
Why this is correct
Stratified sampling preserves class proportions, enabling meaningful EDA.
Related concept
Read the scenario before looking for a memorised answer.
✗
Focus only on the majority class features to avoid bias.
Why it's wrong here
Ignoring minority class is not appropriate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think random sampling (Option A) is sufficient for EDA, but they overlook that severe class imbalance (99:1) makes random samples uninformative for the minority class, whereas stratified sampling explicitly addresses this by ensuring both classes are represented in the analysis subset.
Detailed technical explanation
How to think about this question
Stratified sampling ensures that each class is proportionally represented in the sample, which is essential for imbalanced datasets where random sampling could yield a sample with zero positive instances. Correlation matrices on a balanced subset (e.g., via downsampling the majority class) provide unbiased estimates of feature-target associations, while box plots reveal distributional differences between classes. In practice, this technique is often a precursor to applying SMOTE or cost-sensitive learning, as it helps identify which features have the most predictive power for the rare class.
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
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 sampling to create a balanced subset, then compute correlation matrices and box plots. — Option C is correct because stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.
What should I do if I get this MLS-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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