- A
Lasso regression (L1 regularization)
Lasso efficiently selects features by shrinking coefficients to zero.
- B
Mutual information
Why wrong: Mutual information does not account for multicollinearity among features.
- C
Recursive feature elimination (RFE)
Why wrong: RFE is computationally expensive with many features.
- D
Principal component analysis (PCA)
Why wrong: PCA creates new features, not selection of original ones.
Question 576 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company runs a regression model to predict…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 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?
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.
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.
- ✓
Lasso regression (L1 regularization)
Why this is correct
Lasso efficiently selects features by shrinking coefficients to zero.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
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 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'.
Detailed technical explanation
How to think about this question
Lasso regression adds an L1 penalty (sum of absolute coefficient values) to the loss function, which forces some coefficients to exactly zero due to the geometry of the constraint region (diamond shape in 2D). This property makes it particularly effective when dealing with high-cardinality categorical features like 'zip_code' after one-hot encoding, as it can automatically discard irrelevant or redundant dummy variables. In practice, Lasso may struggle when the number of features exceeds the number of samples, but for 50 features it remains computationally efficient and stable.
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.
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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: 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.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
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