Quick Answer
The answer is to add more relevant features and increase model complexity. This works because high bias, or underfitting, occurs when a model is too simple to capture the underlying data patterns, and linear regression’s default linear form often lacks the capacity to model non-linear relationships. By introducing polynomial features, interaction terms, or domain-specific variables, you directly increase the model’s flexibility, allowing it to fit the training data more closely and reduce bias. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of the bias-variance tradeoff, a core topic in model tuning. A common trap is to add more training data, which helps with high variance (overfitting), not high bias—so remember that bias is a problem of model simplicity, not data quantity. A useful memory tip: “Bias is about brains, not data—give the model more brainpower with better features.”
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Increase the regularization strength.
Why wrong: Increasing regularization increases bias.
Reduce the amount of training data.
Why wrong: Reducing data can increase bias.
Decrease the number of model parameters.
Why wrong: Decreasing parameters reduces complexity and increases bias.
Add more relevant features and increase model complexity.
Adding features reduces 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 relevant features and increase model complexity.
High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.
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.
Increase the regularization strength.
Why it's wrong here
Increasing regularization increases bias.
Reduce the amount of training data.
Why it's wrong here
Reducing data can increase bias.
Decrease the number of model parameters.
Why it's wrong here
Decreasing parameters reduces complexity and increases bias.
Add more relevant features and increase model complexity.
Why this is correct
Adding features reduces bias.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse regularization (which controls overfitting) with bias reduction, mistakenly thinking increasing regularization or reducing parameters will fix underfitting, when in fact those actions increase bias.
Detailed technical explanation
Under the hood, linear regression with ordinary least squares (OLS) minimizes the sum of squared residuals; high bias occurs when the model's hypothesis space is too restricted (e.g., only linear terms). Adding polynomial features or interaction terms expands the hypothesis space, allowing the model to capture non-linear relationships. In real-world scenarios, this is why feature engineering (e.g., adding squared terms for price elasticity) is a common first step before resorting to more complex algorithms.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 relevant features and increase model complexity. — High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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