The correct answer is to apply L2 regularization, also known as Ridge regression, because it directly addresses linear regression overfitting by penalizing large coefficients, which forces the model to shrink less important feature weights toward zero without eliminating them entirely. This technique is a form of L2 regularization feature selection that reduces model variance, improving generalization from a training R-squared of 0.99 to a more realistic test performance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of bias-variance tradeoff and regularization methods—a common trap is confusing L2 with L1 (Lasso), which performs actual feature selection by zeroing out coefficients, whereas L2 retains all features but reduces their impact. Remember the memory tip: L2 is like a gentle shrink wrap that squeezes coefficients but keeps every feature in the game, while L1 is a pruning shear that cuts features out entirely.
MLS-C01 Modeling Practice Question
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 linear regression model on a dataset with 10 numerical features. After training, the model's R-squared value is 0.99 on the training set but only 0.60 on the test set. Which TWO of the following are appropriate actions to reduce overfitting? (Choose TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a subset of the most important features
Regularization (L1 or L2) penalizes large coefficients and reduces overfitting. Reducing model complexity by using fewer features or simplifying the model also helps. Adding more features would increase complexity and overfitting. Increasing the number of epochs is not relevant for linear regression (which has a closed-form solution).
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.
✗
Normalize the features
Why it's wrong here
Normalization helps with convergence but does not directly reduce overfitting.
✗
Add more features to the model
Why it's wrong here
Adding features increases model complexity and overfitting.
✓
Use a subset of the most important features
Why this is correct
Reducing the number of features reduces model complexity and overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of training epochs
Why it's wrong here
Linear regression does not use epochs; it has a closed-form solution.
✓
Apply L2 regularization (Ridge regression)
Why this is correct
L2 regularization penalizes large coefficients, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Use a subset of the most important features — Regularization (L1 or L2) penalizes large coefficients and reduces overfitting. Reducing model complexity by using fewer features or simplifying the model also helps. Adding more features would increase complexity and overfitting. Increasing the number of epochs is not relevant for linear regression (which has a closed-form solution).
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?
easy
✓ A.Use L2 regularization
B.Add more features
C.Use a simpler model
D.Use a deeper decision tree
Why A: L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces model complexity and variance by preventing any single feature from having an overly large influence, without eliminating features entirely, thus keeping bias relatively low.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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