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 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?
Reduce the amount of training data
Why wrong: Less data does not reduce bias; it may increase variance.
Add more features, such as polynomial features
Adding features increases model complexity, reducing bias.
Increase the regularization strength
Why wrong: Stronger regularization increases bias.
Use a simpler model, such as ridge regression
Why wrong: Simpler models have higher bias.
Answer choices
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.
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.
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.
Related concept
Read the scenario before looking for a memorised answer.
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
Simpler models have higher bias.
Common exam traps
Cisco 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.
Detailed technical explanation
Under the hood, linear regression minimizes the sum of squared residuals; when the true relationship is non-linear, the model's hypothesis space is too limited, leading to high bias. Adding polynomial features (e.g., x^2, x^3) expands the feature space into a higher-dimensional basis, enabling the model to approximate curvature. In practice, this is often combined with regularization (e.g., polynomial ridge regression) to control overfitting, but the immediate fix for underfitting is to increase model capacity.
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: 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.
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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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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