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MLS-C01 Modeling Practice Question

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?

⚠ Common exam trap

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.

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

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.

Answer analysis

Option-by-option breakdown

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.

  • 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.

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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.