Quick Answer
The answer is to apply L2 regularization to the model. This technique, also known as Ridge regression, directly combats high variance by adding a penalty term equal to the squared magnitude of the coefficients to the loss function, which shrinks the weights and forces the model to be less sensitive to noise in the training data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the bias-variance tradeoff and the specific role of regularization in regression; a common trap is confusing L2 with L1 regularization (Lasso), which is used for feature selection and can increase bias more aggressively. To remember: L2 is the "smooth shrinker" that reduces variance by penalizing large coefficients without zeroing them out, so think of it as a gentle tug that keeps the model from overfitting.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 building a regression model to predict house prices. The dataset has 10 features, and the model shows high variance with a low bias. Which technique should the data scientist use to reduce variance?
Apply L2 regularization to the model.
L2 regularization reduces variance by penalizing large coefficients.
Increase the depth of decision trees in the ensemble.
Why wrong: Deeper trees increase variance.
Add more features to the model.
Why wrong: Adding more features increases variance.
Reduce the amount of training data.
Why wrong: Less training data increases 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
Apply L2 regularization to the model.
L2 regularization (Ridge regression) penalizes large coefficients by adding a squared magnitude term to the loss function, which shrinks the model's weights and reduces variance without substantially increasing bias. This directly addresses the high-variance, low-bias symptom, making the model less sensitive to fluctuations in the training data.
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.
Apply L2 regularization to the model.
Why this is correct
L2 regularization reduces variance by penalizing large coefficients.
Related concept
Read the scenario before looking for a memorised answer.
Increase the depth of decision trees in the ensemble.
Why it's wrong here
Deeper trees increase variance.
Add more features to the model.
Why it's wrong here
Adding more features increases variance.
Reduce the amount of training data.
Why it's wrong here
Less training data increases variance.
Common exam traps
Cisco often tests the misconception that adding more data or features always reduces variance, but the trap here is that high variance is best addressed by regularization or simplifying the model, not by increasing complexity or reducing data.
Detailed technical explanation
L2 regularization adds a penalty term λ * Σ(w_i²) to the loss function, where λ controls the regularization strength; a larger λ forces weights closer to zero, reducing model complexity. In practice, L2 regularization is particularly effective when features are correlated, as it distributes weight among them rather than selecting a single feature, which helps stabilize predictions. A real-world scenario is predicting house prices with many correlated features (e.g., square footage and number of rooms), where L2 regularization prevents the model from overfitting to noise in the training set.
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: Apply L2 regularization to the model. — L2 regularization (Ridge regression) penalizes large coefficients by adding a squared magnitude term to the loss function, which shrinks the model's weights and reduces variance without substantially increasing bias. This directly addresses the high-variance, low-bias symptom, making the model less sensitive to fluctuations in the training data.
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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Same concept, more angles
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 building a regression model to predict house prices. The dataset contains 10 features, including 'number_of_bedrooms' and 'square_footage'. The scientist observes that the model has high variance. Which TWO actions are most appropriate to reduce overfitting? (Choose TWO.)
mediumWhy A: Options A and C are correct. Adding L2 regularization penalizes large weights, reducing variance. Reducing model complexity (e.g., using a simpler model) also reduces overfitting. Option B is wrong because adding more features increases complexity. Option D is wrong because increasing training epochs may lead to more overfitting. Option E is wrong because decreasing training data increases variance.
Variation 2. A data scientist is building a regression model to predict house prices. The dataset contains features like number of bedrooms, square footage, and location. After training, the model has high variance. Which technique should the data scientist use to reduce variance without significantly increasing bias?
hardWhy C: Option D is correct because L2 regularization (Ridge) penalizes large coefficients, reducing variance while keeping bias low. Option A is wrong because increasing model complexity increases variance. Option B is wrong because removing features may increase bias. Option C is wrong because bagging reduces variance but may not be appropriate for all cases; regularization is more direct.
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Last reviewed: Jun 24, 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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