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.
During EDA, a data scientist discovers that two numerical features have a Pearson correlation coefficient of 0.95. Which action should the scientist take to avoid multicollinearity in a linear regression model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove one of the features
High correlation indicates multicollinearity, which can be addressed by removing one of the correlated features. Option A is wrong because PCA reduces dimensionality but loses interpretability. Option B is wrong because regularization (e.g., Ridge) can handle multicollinearity but does not remove it; removing one feature is simpler. Option D is wrong because polynomial features introduce more multicollinearity. Option E is wrong because scaling does not address correlation.
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 one of the features
Why this is correct
Removing one feature eliminates multicollinearity and retains interpretability.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply PCA to the two features
Why it's wrong here
PCA reduces dimensionality but the components are not interpretable as original features.
✗
Use Ridge regression to penalize coefficients
Why it's wrong here
Ridge regression handles multicollinearity but does not remove it; feature removal is more straightforward.
✗
Create polynomial features from the correlated pair
Why it's wrong here
Polynomial features increase correlation and multicollinearity.
✗
Apply min-max scaling to both features
Why it's wrong here
Scaling does not affect 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: Remove one of the features — High correlation indicates multicollinearity, which can be addressed by removing one of the correlated features. Option A is wrong because PCA reduces dimensionality but loses interpretability. Option B is wrong because regularization (e.g., Ridge) can handle multicollinearity but does not remove it; removing one feature is simpler. Option D is wrong because polynomial features introduce more multicollinearity. Option E is wrong because scaling does not address correlation.
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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