The answer is to calculate the Variance Inflation Factor (VIF) for each feature and to examine the correlation matrix between features. These two methods are correct because multicollinearity detection relies on identifying strong linear dependencies among predictors; the correlation matrix reveals pairwise relationships with high coefficients (e.g., above 0.8 or below -0.8), while VIF quantifies how much a feature’s variance is inflated due to correlation with other features, with values above 5 or 10 signaling problematic multicollinearity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of linear regression assumptions and feature selection, often appearing in a scenario where a data scientist must stabilize coefficient estimates. A common trap is confusing multicollinearity detection with feature importance methods like coefficients or p-values, which are unreliable when collinearity exists. Memory tip: think of the correlation matrix as the “pairwise radar” and VIF as the “inflation gauge”—together they catch both direct and compound collinearity.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 training a linear regression model and wants to check for multicollinearity among the features. Which TWO methods can be used to detect multicollinearity? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Compute the correlation matrix between features
Option B is correct because computing the correlation matrix between features directly reveals pairwise linear relationships. High correlation coefficients (e.g., >0.8 or <-0.8) between two predictors indicate potential multicollinearity, which can destabilize coefficient estimates in linear regression.
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.
✗
Examine the R-squared value of the model
Why it's wrong here
R-squared measures the proportion of variance explained, not collinearity among predictors.
✓
Compute the correlation matrix between features
Why this is correct
High pairwise correlations between features (e.g., >0.8) suggest multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Check the p-values of the coefficients
Why it's wrong here
P-values indicate whether a coefficient is statistically significant, not collinearity.
✓
Calculate Variance Inflation Factor (VIF) for each feature
Why this is correct
VIF quantifies how much the variance of a coefficient is inflated due to collinearity; a high VIF indicates multicollinearity.
Related concept
Read the scenario before looking for a memorised answer.
✗
Plot the residuals vs. fitted values
Why it's wrong here
Residual plots are used to check for homoscedasticity and linearity, not multicollinearity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between diagnosing model fit (R-squared, residual plots) and diagnosing predictor multicollinearity, leading candidates to mistakenly choose methods that evaluate model performance rather than feature interdependence.
Detailed technical explanation
How to think about this question
Variance Inflation Factor (VIF) quantifies how much the variance of a regression coefficient is inflated due to correlation with other predictors; a VIF above 5 or 10 is a common threshold for concern. Under the hood, VIF is calculated by regressing each feature against all other features and using 1/(1-R²) from that auxiliary regression. In real-world scenarios, high VIF can cause coefficient signs to flip or become nonsensical, especially in datasets with many correlated economic indicators.
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.
TExam Day Tips
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Compute the correlation matrix between features — Option B is correct because computing the correlation matrix between features directly reveals pairwise linear relationships. High correlation coefficients (e.g., >0.8 or <-0.8) between two predictors indicate potential multicollinearity, which can destabilize coefficient estimates in linear regression.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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