The correct conclusions are that the two features have a strong linear relationship and that this indicates multicollinearity. A Pearson correlation coefficient of 0.95 measures the strength and direction of a linear association between two continuous variables, with values near +1 confirming a very strong positive linear trend. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to distinguish correlation from causation and to recognize multicollinearity as a key concern during exploratory data analysis, especially when preparing features for linear models like regression. A common trap is assuming high correlation implies one feature causes the other, or that they move in opposite directions—both are false. Remember the memory tip: “High r means linear, not causal; check for collinearity before you model.”
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 generates a pairplot of the dataset and observes that two features have a Pearson correlation coefficient of 0.95. Which TWO conclusions can the scientist draw from this observation? (Choose 2)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The two features may be multicollinear
Options B and C are correct because a high correlation indicates a strong linear relationship and suggests multicollinearity. Option A is wrong because correlation does not imply causation. Option D is wrong because a high positive correlation means they move together, not opposite. Option E is wrong because correlation measures linear relationship, not independence.
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.
✓
The two features may be multicollinear
Why this is correct
High correlation between features can cause multicollinearity in regression models.
Related concept
Read the scenario before looking for a memorised answer.
✓
The two features have a strong linear relationship
Why this is correct
A correlation of 0.95 indicates a strong positive linear relationship.
Related concept
Read the scenario before looking for a memorised answer.
✗
The two features move in opposite directions
Why it's wrong here
A positive correlation means they move in the same direction.
✗
The two features are statistically independent
Why it's wrong here
High correlation indicates dependence, not independence.
✗
One feature causes the other
Why it's wrong here
Correlation does not imply causation.
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: The two features may be multicollinear — Options B and C are correct because a high correlation indicates a strong linear relationship and suggests multicollinearity. Option A is wrong because correlation does not imply causation. Option D is wrong because a high positive correlation means they move together, not opposite. Option E is wrong because correlation measures linear relationship, not independence.
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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