Question 1,352 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model to predict house prices. The dataset contains 10 features. After training, the data scientist notices that the model has high bias (underfitting). Which action should the data scientist take to reduce bias?
⚠ Common exam trap
The MLS-C01 exam often tests the bias-variance tradeoff by making candidates confuse regularization (which reduces variance) with the need to increase model complexity to fix underfitting; the trap here is that increasing regularization or using a simpler model seems like a 'safe' choice, but it actually worsens bias.
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
✓
Add more features, such as polynomial features
High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features, such as polynomial features, increases model complexity, allowing the linear regression model to fit non-linear relationships and reduce bias. This directly addresses the underfitting issue by giving the model more expressive power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the amount of training data
Why it's wrong here
Less data does not reduce bias; it may increase variance.
- ✓
Add more features, such as polynomial features
Why this is correct
Adding features increases model complexity, reducing bias.
- ✗
Increase the regularization strength
Why it's wrong here
Stronger regularization increases bias.
- ✗
Use a simpler model, such as ridge regression
Why it's wrong here
Ridge regression adds an L2 penalty term to the loss function, which shrinks coefficients towards zero, intentionally increasing bias to reduce variance. This directly worsens the existing high bias (underfitting) problem, as the model becomes even less flexible and fails to capture patterns in the training data. It is tempting because ridge regression is a standard remedy for overfitting (high variance) when a linear model has too many features or multicollinearity; in a high-variance scenario, introducing bias via regularisation would correctly improve generalisation.
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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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