The correct answer is to remove one of the correlated features. This is because a strong correlation of 0.95 between two numeric features introduces multicollinearity into a linear regression model, which inflates the variance of coefficient estimates and makes the model unstable and difficult to interpret. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering and the specific pitfalls of linear models, often appearing as a trap where candidates mistakenly choose scaling or regularization as the first step. A common memory tip is to remember that for linear regression, correlated features are redundant—they tell the same story, so you only need one. Think of it as "one story, one feature" to avoid multicollinearity.
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.
During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?
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 correlated features.
Option C is correct because high correlation indicates multicollinearity, which can be addressed by removing one feature. Option A is wrong because scaling does not help. Option B is wrong because interaction terms increase multicollinearity. Option D is wrong because regularization helps but is not the first step; removal is simpler.
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.
✗
Apply standardization to both features.
Why it's wrong here
Standardization does not reduce multicollinearity.
✓
Remove one of the correlated features.
Why this is correct
Removing reduces multicollinearity in linear regression.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use L2 regularization (Ridge regression) without removing features.
Why it's wrong here
Ridge regression can handle multicollinearity but removal is more straightforward.
✗
Create an interaction term between the two features.
Why it's wrong here
Interaction may worsen multicollinearity.
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 correlated features. — Option C is correct because high correlation indicates multicollinearity, which can be addressed by removing one feature. Option A is wrong because scaling does not help. Option B is wrong because interaction terms increase multicollinearity. Option D is wrong because regularization helps but is not the first step; removal is simpler.
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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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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