The correct answer is to perform hierarchical clustering on the correlation matrix and select one feature per cluster. This technique directly addresses the problem of removing highly correlated features by grouping features whose pairwise correlations exceed a threshold, such as 0.95, into clusters based on their similarity. Once the clusters are formed, you can retain a single representative feature from each group, effectively eliminating redundancy without transforming the original features. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering and multicollinearity handling, often appearing as a trap where candidates mistakenly choose PCA or Lasso. PCA creates new components rather than removing original features, and Lasso struggles with groups of highly correlated predictors. A useful memory tip is to think of hierarchical clustering as “grouping the twins” — when features are nearly identical, you only need one from each family.
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 exploring a dataset with 200 features. They compute the pairwise correlation matrix and notice that many features have correlations above 0.95. They want to reduce redundancy before modeling. Which of the following techniques is most appropriate for identifying and removing highly correlated features?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Perform hierarchical clustering on the correlation matrix and select one feature per cluster.
Option D is correct because hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because PCA creates new features but does not remove original ones. Option B is wrong because Lasso performs feature selection but may not handle multicollinearity well. Option C is wrong because mutual information does not capture pairwise redundancy directly.
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.
✗
Compute mutual information between each feature and the target.
Why it's wrong here
Mutual information assesses relevance to target, not redundancy among features.
✗
Apply PCA and keep the first 50 components.
Why it's wrong here
PCA creates new features that are combinations of original ones, losing interpretability.
✗
Use Lasso regression to select features.
Why it's wrong here
Lasso can select features but may arbitrarily choose among correlated ones.
✓
Perform hierarchical clustering on the correlation matrix and select one feature per cluster.
Why this is correct
This systematically removes redundancy while retaining representative features.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Perform hierarchical clustering on the correlation matrix and select one feature per cluster. — Option D is correct because hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because PCA creates new features but does not remove original ones. Option B is wrong because Lasso performs feature selection but may not handle multicollinearity well. Option C is wrong because mutual information does not capture pairwise redundancy directly.
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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