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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
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.
✓
Apply L1 regularization (Lasso)
Why this is correct
L1 regularization performs feature selection, reducing overfitting and keeping the model interpretable.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
L1 regularization works by adding a penalty term λ * Σ|w_i| to the loss function, where λ controls the strength of regularization. During optimization, the non-differentiability at zero causes many coefficients to become exactly zero, effectively performing feature selection. In high-dimensional settings (500 features vs 100,000 samples), this is particularly effective at identifying the most predictive features while discarding noise, and the resulting sparse model is easier to interpret because only a subset of features remain.
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: 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.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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