The correct answer involves using a correlation matrix, pair plots, and box plots by target, as these three techniques are fundamental for feature-target relationship analysis in EDA. A correlation matrix quantifies linear relationships between numeric features and the target, while pair plots allow visual inspection of multiple feature interactions simultaneously, and box plots grouped by the target variable reveal distribution differences across categories. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish true EDA techniques from modeling or unsupervised methods—common traps include selecting PCA, which is for dimensionality reduction, or clustering, which is unsupervised and not directly for feature-target relationships. Remember that EDA for relationships focuses on visualization and statistical summaries, not transformation or grouping algorithms. A helpful memory tip: “Correlate, visualize, compare” to recall correlation matrices, pair plots, and box plots by target.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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.
Which THREE techniques are commonly used in exploratory data analysis to understand the relationships between features and the target variable? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use box plots to compare feature distributions across target classes.
Options A, C, and E are correct. A: Correlation matrix quantifies linear relationships. C: Pair plots allow visual inspection of multiple relationships. E: Box plots by target show distribution differences. B is wrong because PCA is for dimensionality reduction, not EDA of relationships. D is wrong because clustering is unsupervised and not directly for feature-target relationships.
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 box plots to compare feature distributions across target classes.
Why this is correct
Box plots by class reveal differences in feature distributions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Perform K-means clustering on the features.
Why it's wrong here
Clustering groups data, not for feature-target relationship.
✓
Compute the correlation matrix between features and target.
Why this is correct
Correlation measures linear relationship strength.
Related concept
Read the scenario before looking for a memorised answer.
✓
Generate scatter plots or pair plots to visualize feature interactions.
Why this is correct
Pair plots show pairwise relationships including with target.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply Principal Component Analysis (PCA) to reduce dimensions.
Why it's wrong here
PCA is not primarily for understanding feature-target relationships.
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
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 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 box plots to compare feature distributions across target classes. — Options A, C, and E are correct. A: Correlation matrix quantifies linear relationships. C: Pair plots allow visual inspection of multiple relationships. E: Box plots by target show distribution differences. B is wrong because PCA is for dimensionality reduction, not EDA of relationships. D is wrong because clustering is unsupervised and not directly for feature-target relationships.
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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