Quick Answer
The answer is L1 regularization, also known as Lasso. This technique is correct because it adds a penalty equal to the absolute value of the coefficients to the loss function, which can shrink some coefficients exactly to zero, effectively performing feature selection by removing irrelevant or redundant predictors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how different regularization methods handle multicollinearity and overfitting in regression models—a common trap is confusing L2 (Ridge) regularization, which penalizes squared coefficients but never zeros them out, making it unsuitable for feature selection. A reliable memory tip is to associate Lasso with “zeroing out” features: think of the absolute value penalty as a “lasso” that lassos and eliminates unimportant variables, while Ridge only shrinks them.
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 building a regression model to predict house prices. The dataset contains many features, some of which are highly correlated. The model is overfitting. Which regularization technique should the scientist use to penalize large coefficients and perform feature selection?
L2 regularization (Ridge)
Why wrong: L2 regularization does not zero out coefficients.
L1 regularization (Lasso)
L1 regularization can zero out coefficients, performing feature selection.
Elastic Net regularization
Why wrong: Elastic Net combines L1 and L2, but L1 alone is sufficient for feature selection.
Dropout
Why wrong: Dropout is used in neural networks, not linear regression.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
L1 regularization (Lasso)
L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients to zero, performing feature selection. L2 regularization (Ridge) penalizes squared coefficients but does not zero them out. Elastic Net combines both. Dropout is for neural networks. Option A: L1 regularization is correct. Option B: L2 regularization does not perform feature selection. Option C: Elastic Net combines both but L1 alone is simpler for feature selection. Option D: Dropout is not applicable to linear regression.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
L2 regularization (Ridge)
Why it's wrong here
L2 regularization does not zero out coefficients.
L1 regularization (Lasso)
Why this is correct
L1 regularization can zero out coefficients, performing feature selection.
Related concept
Read the scenario before looking for a memorised answer.
Elastic Net regularization
Why it's wrong here
Elastic Net combines L1 and L2, but L1 alone is sufficient for feature selection.
Dropout
Why it's wrong here
Dropout is used in neural networks, not linear regression.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: L1 regularization (Lasso) — L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients to zero, performing feature selection. L2 regularization (Ridge) penalizes squared coefficients but does not zero them out. Elastic Net combines both. Dropout is for neural networks. Option A: L1 regularization is correct. Option B: L2 regularization does not perform feature selection. Option C: Elastic Net combines both but L1 alone is simpler for feature selection. Option D: Dropout is not applicable to linear regression.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 2026
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.
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