The correct actions are to remove one of the correlated features or combine them into a single feature. This is because a Pearson correlation coefficient of 0.95 indicates severe multicollinearity, which destabilizes model coefficients and inflates variance, making interpretation unreliable. Removing one feature eliminates redundancy, while combining them (e.g., via PCA or averaging) preserves information without the statistical noise. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering and the pitfalls of high correlation, often appearing in questions about regression or linear models. A common trap is confusing regularization—which mitigates multicollinearity’s effects but does not remove the correlation itself—with direct feature handling. Remember the mnemonic: “Correlation high? Combine or say goodbye.”
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 scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Combine the two features into a single feature using PCA or averaging
Options B and C are correct. High correlation can lead to multicollinearity, so removing one feature (B) or combining them (C) are valid approaches. Option A is wrong because increasing regularization is a remedy for multicollinearity but does not directly address the correlation. Option D is wrong because scaling does not affect correlation. Option E is wrong because adding interaction terms can increase multicollinearity.
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.
✓
Combine the two features into a single feature using PCA or averaging
Why this is correct
Combining captures information from both while reducing dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add interaction terms between the features
Why it's wrong here
Interaction terms may increase multicollinearity.
✗
Increase regularization strength in the model
Why it's wrong here
Regularization can help with multicollinearity but is not the primary action for correlated features.
✓
Remove one of the correlated features
Why this is correct
Removing one reduces redundancy and multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply standard scaling to both features
Why it's wrong here
Scaling does not change correlation.
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: Combine the two features into a single feature using PCA or averaging — Options B and C are correct. High correlation can lead to multicollinearity, so removing one feature (B) or combining them (C) are valid approaches. Option A is wrong because increasing regularization is a remedy for multicollinearity but does not directly address the correlation. Option D is wrong because scaling does not affect correlation. Option E is wrong because adding interaction terms can increase multicollinearity.
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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