The answer is to take a stratified sample that preserves the 99:1 ratio. This is the correct approach because stratified sampling ensures that the rare positive class is proportionally represented in your sample, preventing the exploratory data analysis from being skewed or missing critical patterns in the minority class. When handling an imbalanced dataset during EDA with stratified sampling, you maintain the original distribution without artificially altering the data, which is essential for honest visualization and initial statistical checks. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to avoid common pitfalls like random sampling, which could accidentally exclude all positive examples, or up-sampling, which would distort the true class distribution before modeling. A common trap is assuming you need to fix the imbalance immediately during EDA, but the exam emphasizes that EDA should reflect the real data. Memory tip: "Stratify to verify" — always preserve the original ratio when exploring, then decide on resampling strategies later.
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 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Take a stratified sample that preserves the 99:1 ratio.
Option B is correct because stratified sampling preserves the class proportion in the sample, which is critical for imbalanced data. Option A (random sample) may miss positives; Option C (up-sample minority) changes distribution; Option D (remove negatives) loses information.
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.
✗
Remove all negative examples and analyze only the positive ones.
Why it's wrong here
Removing negatives discards most of the data.
✗
Take a random sample of 100,000 rows from the entire dataset.
Why it's wrong here
Random sampling may not capture enough positive examples.
✓
Take a stratified sample that preserves the 99:1 ratio.
Why this is correct
Stratified sampling ensures representation of both classes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Up-sample the minority class to balance the dataset before analysis.
Why it's wrong here
Up-sampling before EDA can introduce artificial patterns.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Take a stratified sample that preserves the 99:1 ratio. — Option B is correct because stratified sampling preserves the class proportion in the sample, which is critical for imbalanced data. Option A (random sample) may miss positives; Option C (up-sample minority) changes distribution; Option D (remove negatives) loses information.
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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