hardMultiple Choice
MLA-C01 Practice Question: A company runs a regression model to predict…
A company runs a regression model to predict house prices. They have 50 features including 'zip_code' (high cardinality), 'square_footage', and 'year_built'. They want to select the most important features to reduce overfitting. Which feature selection method is computationally efficient for high-dimensional data and can handle multicollinearity?
⚠ Common exam trap
The AWS ML Engineer Associate exam often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so candidates mistakenly choose PCA thinking it handles multicollinearity, but PCA transforms features rather than selecting them, which violates the requirement to 'select the most important features'.
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
✓
Lasso regression (L1 regularization)
Lasso regression (L1 regularization) is computationally efficient for high-dimensional data because it performs both feature selection and regularization simultaneously by shrinking less important feature coefficients to zero. It can handle multicollinearity by selecting only one feature from a correlated group, effectively reducing overfitting while maintaining model interpretability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Lasso regression (L1 regularization)
Why this is correct
Lasso efficiently selects features by shrinking coefficients to zero.
- ✗
Mutual information
Why it's wrong here
Mutual information does not account for multicollinearity among features.
- ✗
Recursive feature elimination (RFE)
Why it's wrong here
RFE is computationally expensive with many features.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA creates new features, not selection of original ones.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.