MLS-C01 Modeling Practice Question
A data scientist notices that a linear regression model trained on a dataset has high variance. The model performs well on the training data but poorly on the test data. Which action is most likely to reduce the variance?
⚠ Common exam trap
The MLS-C01 exam often tests the bias-variance tradeoff by making candidates confuse regularization with optimization steps or feature engineering, so the trap here is assuming that more training data or more iterations always improve model performance without considering their effect on variance.
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
High variance indicates the model is overfitting to the training data. L2 regularization (ridge regression) adds a penalty proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces the model's sensitivity to noise in the training data, thereby lowering variance and improving generalization to the test set.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the amount of training data
Why it's wrong here
Using less training data generally increases variance because the model has fewer examples to learn from.
- ✓
Apply L2 regularization to the model
Why this is correct
L2 regularization shrinks coefficients and reduces model complexity, thereby reducing variance.
- ✗
Increase the number of gradient descent iterations
Why it's wrong here
More iterations may lead to overfitting if not regularized, potentially increasing variance.
- ✗
Add more features to the model
Why it's wrong here
Adding more features increases model complexity, which can increase variance.
Go deeper
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.