Question 1,374 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier using logistic regression. The dataset has 100,000 samples and 500 features. After training, the model achieves 95% accuracy on the training set but only 70% on the test set. The data scientist suspects overfitting. Which technique would best reduce overfitting while preserving interpretability?
⚠ Common exam trap
AWS often tests the distinction between regularization techniques that shrink coefficients (L2/Ridge) versus those that zero them out (L1/Lasso), and candidates may mistakenly choose L2 or fail to recognize that L1 directly improves interpretability by removing irrelevant 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
✓
Apply L1 regularization (Lasso)
L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which drives many feature weights to exactly zero. This performs automatic feature selection, reducing model complexity and overfitting while keeping the model as a simple linear logistic regression, thus preserving 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.
- ✓
Apply L1 regularization (Lasso)
Why this is correct
L1 regularization performs feature selection, reducing overfitting and keeping the model interpretable.
- ✗
Increase the maximum number of iterations
Why it's wrong here
More iterations do not reduce overfitting.
- ✗
Add polynomial features
Why it's wrong here
Adding polynomial features increases model complexity, likely increasing overfitting.
- ✗
Use a random forest model instead
Why it's wrong here
Random forest is less interpretable than logistic regression.
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Last reviewed: Jun 30, 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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