The answer is generating a correlation matrix heatmap and calculating Variance Inflation Factor (VIF). A correlation matrix heatmap visually reveals strong pairwise linear relationships between features, while VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity, with values above 5 or 10 indicating problematic collinearity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between exploratory techniques that directly detect multicollinearity and modeling steps that address it later—a common trap is confusing Lasso regularization or PCA, which reduce dimensionality or penalize coefficients, with actual detection methods. Remember that correlation matrices and VIF are diagnostic tools used during EDA, not model training. A useful memory tip: “VIF and heatmap spot the overlap; PCA and Lasso fix the slope.”
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 machine learning engineer is analyzing a dataset with 500 features and suspects multicollinearity. Which TWO techniques can help identify and address multicollinearity during exploratory data analysis? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Calculate Variance Inflation Factor (VIF) for each feature
Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity. Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.
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 t-SNE for visualization
Why it's wrong here
t-SNE is for visualization, not for detecting multicollinearity.
✗
Apply Principal Component Analysis (PCA)
Why it's wrong here
PCA transforms features but does not identify multicollinearity.
✓
Calculate Variance Inflation Factor (VIF) for each feature
Why this is correct
VIF > 5-10 indicates multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✓
Generate a correlation matrix heatmap
Why this is correct
High pairwise correlations indicate multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Lasso regression to select features
Why it's wrong here
Lasso addresses multicollinearity but is a modeling technique, not EDA.
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: Calculate Variance Inflation Factor (VIF) for each feature — Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity. Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?
easy
A.Perform PCA on the dataset to reduce dimensionality.
B.Add an interaction term between X1 and X2 to the model.
C.Standardize both features using Z-score normalization.
✓ D.Remove one of the two highly correlated features.
Why D: Option C is correct because removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA is not necessary for just two correlated features. Option B is wrong because standard scaling does not address correlation. Option D is wrong because adding interaction terms increases multicollinearity.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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