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 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?
Use L2 regularization
L2 regularization penalizes large coefficients, reducing variance.
Add more features
Why wrong: Adding features increases model complexity, likely increasing variance.
Use a simpler model
Why wrong: Simpler models increase bias, which may not be desired.
Use a deeper decision tree
Why wrong: Deeper trees increase variance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use L2 regularization
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use L2 regularization
Why this is correct
L2 regularization penalizes large coefficients, reducing variance.
Related concept
Read the scenario before looking for a memorised answer.
Add more features
Why it's wrong here
Adding features increases model complexity, likely increasing variance.
Use a simpler model
Why it's wrong here
Simpler models increase bias, which may not be desired.
Use a deeper decision tree
Why it's wrong here
Deeper trees increase variance.
Common exam traps
AWS often tests the distinction between L1 (Lasso) and L2 (Ridge) regularization, and the trap here is that candidates might think adding more features or using a simpler model is the only way to reduce variance, overlooking that L2 regularization can reduce variance without the drastic bias increase of feature elimination.
Detailed technical explanation
L2 regularization works by adding the sum of squared coefficients (scaled by a hyperparameter λ) to the loss function, which forces the model to find a solution that balances fit with coefficient magnitude. In practice, this is equivalent to assuming a Gaussian prior on the weights, and the optimal λ can be found via cross-validation. A real-world scenario is when dealing with multicollinearity in features, where L2 regularization stabilizes coefficient estimates and improves generalization.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use L2 regularization — 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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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