MLS-C01 Modeling Practice Question
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?
⚠ Common exam trap
The MLS-C01 exam 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.
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
✓
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.
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 L2 regularization to the model.
Why this is correct
L2 regularization reduces variance by penalizing large coefficients.
- ✗
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.
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Same concept, more angles
2 more ways this is tested on MLS-C01
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.)
medium- ✓ A.Reduce model complexity by using a simpler model
- ✓ B.Add L2 regularization to the model
- C.Increase the number of training epochs
- D.Decrease the amount of training data
- E.Add more polynomial features
Why A: A is correct because reducing model complexity, such as using a simpler model (e.g., linear regression instead of a high-degree polynomial), directly decreases variance by limiting the model's capacity to fit noise in the training data. This aligns with the bias-variance tradeoff, where simpler models have higher bias but lower variance, making them less prone to overfitting.
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?
hard- A.Use bagging
- B.Increase the number of features
- ✓ C.Apply L2 regularization
- D.Use fewer training examples
Why C: L2 regularization (Ridge) penalizes large coefficients, effectively shrinking them and reducing model complexity, which lowers variance without substantially increasing bias. Option A (bagging) also reduces variance, but the question asks for a technique that reduces variance without significantly increasing bias; regularization is a more direct and common approach for high variance in regression. Option B (increasing features) would increase model complexity and variance, worsening the problem. Option D (fewer training examples) would increase bias and potentially variance due to less data.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.