MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 10 features and observes that the correlation between feature A and feature B is 0.98. Which action should be taken to address multicollinearity before training a linear regression model?
Answer choices
Why each option matters
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 two features from the dataset.
Dropping one of the highly correlated features reduces redundancy and mitigates multicollinearity. Option A (PCA) creates orthogonal components, which addresses multicollinearity but reduces interpretability; dropping a feature is more straightforward. Option B (Min-Max scaling) does not address collinearity at all. Option D is wrong because adding polynomial features increases correlation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Principal Component Analysis (PCA) to combine them.
Why it's wrong here
PCA is not the most appropriate action for multicollinearity between just two features. While it can combine them, its primary mechanism involves transforming a *larger set* of inter-correlated variables into a new, orthogonal set of principal components, primarily for dimensionality reduction. For only two highly correlated features, PCA introduces a transformation that can hinder interpretability of the original features. It is tempting because PCA is a valid technique to address multicollinearity and reduce dimensionality when *many* features exhibit high correlation.
- ✗
Apply Min-Max scaling to both features.
Why it's wrong here
Min-Max scaling does not address collinearity; it only rescales features.
- ✓
Remove one of the two features from the dataset.
Why this is correct
Dropping one of the highly correlated features removes redundancy and mitigates multicollinearity, which is a simple and effective solution.
- ✗
Add polynomial features to both.
Why it's wrong here
Adding polynomial features introduces additional correlation, making multicollinearity worse.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.