Quick Answer
The answer is to add more features or increase model complexity. This directly reduces underfitting bias because high bias occurs when a model is too simplistic to capture the underlying patterns in the data, so giving it greater capacity—through additional input variables, polynomial features, or a more flexible algorithm like XGBoost with deeper trees—allows it to learn more complex relationships. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of the bias-variance tradeoff, often appearing in scenario-based questions where you must distinguish underfitting from overfitting; a common trap is confusing high bias with high variance and incorrectly suggesting regularization or more data. Remember the memory tip: bias is blindness—if your model is too blind to see patterns, open its eyes with more features or complexity.
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 data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows 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.
Reduce the amount of training data
Why wrong: Less data increases bias.
Increase regularization strength
Why wrong: Regularization increases bias.
Use a simpler model
Why wrong: Simpler model increases bias.
Add more features or increase model complexity
More complex models can capture patterns better.
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 or increase model complexity
High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.
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 increases bias.
Increase regularization strength
Why it's wrong here
Regularization increases bias.
Use a simpler model
Why it's wrong here
Simpler model increases bias.
Add more features or increase model complexity
Why this is correct
More complex models can capture patterns better.
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 bias with variance and incorrectly choose regularization or simpler models, which are solutions for overfitting (high variance), not underfitting (high bias).
Detailed technical explanation
Underfitting occurs when the model's hypothesis space is too constrained to represent the true function, often quantified by high training error. In practice, bias-variance tradeoff dictates that adding features (e.g., interaction terms or polynomial expansions) increases variance but decreases bias; for underfit models, the net effect is improved performance. In SageMaker, you might use the 'num_round' or 'max_depth' hyperparameters in XGBoost to increase complexity, or switch from a linear learner to a tree-based algorithm.
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 or increase model complexity — High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.
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 11, 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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