The answer is to add polynomial features or interaction terms to the feature set. This technique directly addresses high bias in linear regression because a linear model with only raw features cannot capture non-linear relationships in the data, leading to underfitting. By introducing polynomial or interaction terms, the model gains the flexibility to fit curvature and complex patterns, thereby reducing bias without increasing variance significantly. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of the bias-variance tradeoff and how feature engineering impacts model performance. A common trap is confusing bias-reduction techniques with variance-reduction techniques: regularization (like Lasso or Ridge) reduces variance, not bias, and adding more data also primarily helps with variance. Remember the memory tip: "Bias is about being too simple, so add complexity; variance is about being too complex, so add simplicity or data."
MLS-C01 Modeling Practice Question
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 using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?
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 or interaction terms to the feature set.
Option B is correct because adding interaction features or polynomial features allows the linear model to capture non-linear relationships, reducing bias. Option A (regularization) reduces variance, not bias. Option C (more data) helps variance. Option D (feature selection) reduces complexity, may increase bias. Option E (reduce learning rate) affects convergence speed, not bias.
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.
✗
Apply L2 regularization (Ridge) to penalize large coefficients.
Why it's wrong here
Regularization reduces variance, not bias.
✓
Add polynomial features or interaction terms to the feature set.
Why this is correct
Increasing model complexity reduces bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the learning rate.
Why it's wrong here
Learning rate does not affect bias.
✗
Use feature selection to remove irrelevant features.
Why it's wrong here
Removing features can increase bias.
✗
Increase the number of training epochs.
Why it's wrong here
More epochs only ensure convergence, not reduce bias.
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.
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 or interaction terms to the feature set. — Option B is correct because adding interaction features or polynomial features allows the linear model to capture non-linear relationships, reducing bias. Option A (regularization) reduces variance, not bias. Option C (more data) helps variance. Option D (feature selection) reduces complexity, may increase bias. Option E (reduce learning rate) affects convergence speed, not bias.
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 →
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 data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the model has a high bias. What is the most likely cause?
medium
A.The training dataset has too many features
B.The model is too complex and overfits the data
C.The regularization parameter is too high
✓ D.The model is too simple and underfits the data
Why D: High bias is typically caused by the model being too simple to capture patterns in the data. Option B is wrong because high variance would cause overfitting. Option C is wrong because regularization reduces overfitting, not bias. Option D is wrong because too many features would increase variance, not bias.
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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.
Question Discussion
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