- A
Increase L2 regularization.
Why wrong: Increasing regularization increases bias by penalizing large coefficients.
- B
Add polynomial features.
Polynomial features increase model capacity, reducing bias.
- C
Reduce the regularization strength.
Lower regularization allows the model to fit the training data more closely, reducing bias.
- D
Use a smaller training dataset.
Why wrong: Less data usually leads to higher bias, not lower.
- E
Use a random forest model instead of linear regression.
Random forest is more complex and can reduce bias if linear assumptions are violated.
Question 1,617 of 1,755
Reduce Bias (Underfitting) in Linear Regression | AWS ML Specialty
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 company uses Amazon SageMaker to train a linear regression model. During evaluation, they observe that the model has high bias (underfitting). Which THREE actions can reduce 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 polynomial features.
Options B, C, and E are correct. Bias (underfitting) occurs when the model is too simple to capture patterns in the data. Adding polynomial features (B) increases model complexity, allowing the linear regression to fit non-linear relationships. Reducing regularization strength (C) reduces the penalty on large coefficients, letting the model fit the training data more closely. Using a random forest model (E) is a more complex algorithm capable of capturing non-linear patterns, thus reducing bias. Option A (increasing L2 regularization) increases bias by penalizing large weights. Option D (using a smaller training dataset) typically increases bias due to less data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase L2 regularization.
Why it's wrong here
Increasing regularization increases bias by penalizing large coefficients.
- ✓
Add polynomial features.
Why this is correct
Polynomial features increase model capacity, reducing bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Reduce the regularization strength.
Why this is correct
Lower regularization allows the model to fit the training data more closely, reducing bias.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller training dataset.
Why it's wrong here
Less data usually leads to higher bias, not lower.
- ✓
Use a random forest model instead of linear regression.
Why this is correct
Random forest is more complex and can reduce bias if linear assumptions are violated.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add polynomial features. — Options B, C, and E are correct. Bias (underfitting) occurs when the model is too simple to capture patterns in the data. Adding polynomial features (B) increases model complexity, allowing the linear regression to fit non-linear relationships. Reducing regularization strength (C) reduces the penalty on large coefficients, letting the model fit the training data more closely. Using a random forest model (E) is a more complex algorithm capable of capturing non-linear patterns, thus reducing bias. Option A (increasing L2 regularization) increases bias by penalizing large weights. Option D (using a smaller training dataset) typically increases bias due to less data.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Amazon SageMaker to train a linear regression model. After training, the model shows high bias on the training set. Which action is MOST likely to reduce bias?
medium- ✓ A.Add more features
- B.Collect more training data
- C.Apply L2 regularization
- D.Deploy the model to a larger instance
Why A: High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more features increases the model's capacity to learn complex relationships, directly addressing underfitting by reducing bias. In SageMaker, this can be done by engineering additional input columns or using feature transformations before training.
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Last reviewed: Jun 20, 2026
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