- A
Recursive Feature Elimination (RFE)
RFE uses a model (e.g., logistic regression) to select a subset of features based on importance, preserving interpretability.
- B
Lasso regularization with cross-validation
Why wrong: Lasso can shrink coefficients to zero, but the number of selected features is not controllable and may not achieve 50% reduction.
- C
Principal Component Analysis (PCA)
Why wrong: PCA creates new components that are not interpretable in terms of original features.
- D
Remove features with mutual information below a threshold
Why wrong: Mutual information selects features relevant to the target, but does not handle correlation among features.
Question 165 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist needs to select features for a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 needs to select features for a customer churn prediction model. The dataset contains 500 features, many of which are highly correlated. The requirement is to reduce the feature set by at least 50% while maintaining model interpretability. Which feature selection technique is MOST appropriate?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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
✓
Recursive Feature Elimination (RFE)
Recursive Feature Elimination (RFE) is the most appropriate technique because it iteratively removes the least important features based on a model's feature importance scores (e.g., from a tree-based model or linear model), directly reducing the feature set by at least 50% while preserving interpretability. Unlike dimensionality reduction methods, RFE retains the original features, making the model's decisions transparent and explainable to stakeholders.
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.
- ✓
Recursive Feature Elimination (RFE)
Why this is correct
RFE uses a model (e.g., logistic regression) to select a subset of features based on importance, preserving interpretability.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Lasso regularization with cross-validation
Why it's wrong here
Lasso can shrink coefficients to zero, but the number of selected features is not controllable and may not achieve 50% reduction.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA creates new components that are not interpretable in terms of original features.
- ✗
Remove features with mutual information below a threshold
Why it's wrong here
Mutual information selects features relevant to the target, but does not handle correlation among features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between feature selection (keeping original features) and dimensionality reduction (creating new features), so the trap here is that candidates may choose PCA (Option C) because it reduces features, but overlook that PCA destroys interpretability by transforming features into unlabeled principal components.
Detailed technical explanation
How to think about this question
RFE works by training a model (e.g., logistic regression or random forest) on the full feature set, ranking features by their importance (e.g., coefficient magnitude or Gini importance), then pruning the least important feature(s) and retraining iteratively until the desired number of features remains. In practice, RFE with cross-validation (RFECV) can automatically select the optimal subset, but for a fixed 50% reduction, a simple RFE with a specified number of features to retain is sufficient. A subtle behavior is that RFE's performance depends heavily on the choice of the underlying estimator; using a linear model ensures interpretability, while tree-based models may introduce non-linear interactions that complicate explanation.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
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 the most appropriate technique because it iteratively removes the least important features based on a model's feature importance scores (e.g., from a tree-based model or linear model), directly reducing the feature set by at least 50% while preserving interpretability. Unlike dimensionality reduction methods, RFE retains the original features, making the model's decisions transparent and explainable to stakeholders.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.