Quick Answer
The answer is to add polynomial features, such as squared terms and interaction terms, to the regression model. This approach directly addresses the need to capture non-linear relationships in regression, which a standard linear model cannot represent. By introducing polynomial features, the model gains the flexibility to fit curves and interactions between variables like temperature and humidity, which is why the test R-squared dropped to 0.40—the linear model was underfitting the non-linear patterns in the data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering for non-linearity versus regularization or dimensionality reduction; a common trap is to confuse underfitting (high bias) with overfitting (high variance) and incorrectly choose L1/L2 regularization. Remember the memory tip: "Polynomials bend lines, regularization just tightens them"—if your model is missing the curve, add polynomial features, not penalties.
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 building a regression model to predict energy consumption. The dataset includes features like temperature, humidity, day of week, and holiday flags. The scientist uses a linear regression model and obtains an R-squared of 0.85 on training and 0.40 on test. The scientist suspects the model is not capturing non-linear relationships. Which approach should the scientist use to capture non-linearity?
Apply PCA to the feature set
Why wrong: PCA is linear transformation, not capturing non-linearity.
Increase L1 regularization using Lasso
Why wrong: Regularization does not add non-linearity.
Remove features with low correlation to the target
Why wrong: Removing features may not help capture non-linearity.
Add polynomial features (e.g., squared terms and interactions)
Polynomial features allow linear model to fit non-linear patterns.
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 polynomial features (e.g., squared terms and interactions)
Option C (add polynomial features) captures non-linear relationships. Option A (increase regularization) reduces overfitting but doesn't add non-linearity. Option B (use PCA) reduces dimensionality. Option D (remove features) may lose information.
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.
Apply PCA to the feature set
Why it's wrong here
PCA is linear transformation, not capturing non-linearity.
Increase L1 regularization using Lasso
Why it's wrong here
Regularization does not add non-linearity.
Remove features with low correlation to the target
Why it's wrong here
Removing features may not help capture non-linearity.
Add polynomial features (e.g., squared terms and interactions)
Why this is correct
Polynomial features allow linear model to fit non-linear patterns.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Add polynomial features (e.g., squared terms and interactions) — Option C (add polynomial features) captures non-linear relationships. Option A (increase regularization) reduces overfitting but doesn't add non-linearity. Option B (use PCA) reduces dimensionality. Option D (remove features) may lose information.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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