Question 292 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist notices that the correlation matrix of features shows many pairs with absolute correlation > 0.95. The dataset includes both numerical and categorical variables. Which technique is most appropriate to reduce multicollinearity while preserving the most information?
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
✓
Apply Principal Component Analysis (PCA) to the features.
PCA is the most appropriate technique because it transforms correlated features into orthogonal principal components, effectively handling multicollinearity while preserving variance. Option B (using only one-hot encoded categorical features) discards numerical features and may not address multicollinearity; Option C (L1 regularization) is a modeling technique, not for EDA; Option D (removing one feature per highly correlated pair) is ad-hoc and can lose information compared to PCA which captures variance in fewer dimensions.
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 Principal Component Analysis (PCA) to the features.
Why this is correct
PCA reduces dimensionality and decorrelates features.
- ✗
Use only one-hot encoded categorical features.
Why it's wrong here
One-hot encoding may still have multicollinearity.
- ✗
Apply L1 regularization during model training.
Why it's wrong here
Regularization is a model training step, not EDA.
- ✗
Remove one feature from each highly correlated pair.
Why it's wrong here
This is arbitrary and may discard important information.
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Last reviewed: Jun 20, 2026
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