easyMultiple Choice
MLA-C01 Practice Question: A data scientist is performing feature selection…
A data scientist is performing feature selection for a linear regression model and wants to remove features that are highly correlated with each other to reduce multicollinearity. Which technique is BEST suited for this purpose?
⚠ Common exam trap
MLA-C01 often tests the confusion between feature-target relevance techniques (mutual information, RFE) and feature-feature redundancy techniques (correlation, VIF) — candidates pick a selection method that optimizes prediction rather than one that detects multicollinearity.
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
✓
Correlation analysis
Correlation analysis directly measures the linear relationship between pairs of features, producing a correlation matrix that identifies highly correlated pairs (e.g., |r| > 0.8) for removal. Since multicollinearity in linear regression is specifically about linear dependence among predictors, correlation analysis is the most direct and interpretable technique for detecting and removing redundant features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Correlation analysis
Why this is correct
Correlation analysis directly quantifies pairwise linear dependence between features, so highly correlated pairs can be identified and pruned before fitting. This satisfies the stem's requirement to reduce multicollinearity in linear regression, where correlated predictors inflate coefficient variance. Other techniques address feature importance or dimensionality differently, not pairwise correlation detection.
- ✗
Lasso regularization
Why it's wrong here
Lasso shrinks coefficients via an L1 penalty, driving some to zero for sparsity, but it selects features by predictive contribution and does not explicitly detect or remove correlated pairs. It is tempting because it performs embedded selection, and would suit high-dimensional prediction where parsimony matters more than diagnosing collinearity.
- ✗
Mutual information
Why it's wrong here
Mutual information measures dependence between each feature and the target, not correlation among predictor variables, so it cannot identify collinear pairs. It is tempting because it is model-agnostic and captures non-linear relationships, and would suit ranking features by relevance to a categorical or non-linear target.
- ✗
Recursive Feature Elimination (RFE)
Why it's wrong here
RFE ranks features by model coefficients or importance and removes the weakest iteratively; correlated features can both survive or be dropped arbitrarily, so it does not resolve multicollinearity. It is tempting because it optimises a model's performance, and would suit selecting a compact feature set for a non-linear estimator.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-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 MLA-C01 exam.