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 performing feature selection for a classification problem with 100 features. The data scientist wants to reduce overfitting and improve model interpretability. Which THREE methods are appropriate for feature selection? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is a wrapper method that recursively removes the least important features based on a model's feature weights or coefficients, training the model multiple times to identify the optimal subset. This directly reduces overfitting by eliminating irrelevant or redundant features and improves interpretability by keeping only the most predictive features.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality by creating new components, not selecting original features.
✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE recursively removes the least important features based on model coefficients or feature importance.
Related concept
Read the scenario before looking for a memorised answer.
✓
L1 regularization (Lasso)
Why this is correct
L1 regularization shrinks some coefficients to zero, effectively selecting a subset of features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Adding random noise to the features
Why it's wrong here
Adding noise does not select features; it may degrade model performance.
✓
Feature importance from a random forest model
Why this is correct
Tree-based models provide feature importance scores that can be used to select top features.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so candidates mistakenly choose PCA as a feature selection method when it is actually a feature extraction technique.
Detailed technical explanation
How to think about this question
RFE works by fitting a model (e.g., logistic regression or SVM) and ranking features by their coefficients or feature importance, then pruning the weakest feature(s) and repeating until a desired number of features remains. L1 regularization (Lasso) performs embedded feature selection by shrinking some coefficients exactly to zero, effectively removing those features, which is computationally efficient for high-dimensional data. Feature importance from a random forest model ranks features by metrics like mean decrease in impurity (Gini importance) or permutation importance, allowing selection of top-k features to reduce dimensionality and overfitting.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recursive Feature Elimination (RFE) — Recursive Feature Elimination (RFE) is a wrapper method that recursively removes the least important features based on a model's feature weights or coefficients, training the model multiple times to identify the optimal subset. This directly reduces overfitting by eliminating irrelevant or redundant features and improves interpretability by keeping only the most predictive features.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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