Quick Answer
The answer is to increase the amount of training data. Adding more training data helps reduce linear regression overfitting on SageMaker because it provides a more representative sample of the underlying distribution, allowing the model to generalize beyond the noise in the original dataset rather than memorizing it. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that overfitting stems from high variance, and the most direct cure is more data—not increasing model complexity, which would worsen the problem. A common trap is to confuse overfitting solutions with optimization tweaks like adjusting learning rate or batch size, which address convergence, not generalization. Remember the memory tip: when validation R² lags far behind training R², think "more data, less drama"—adding observations is the simplest way to combat high variance in linear models.
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 training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?
Increase the amount of training data
More data helps the model generalize better.
Increase the batch size
Why wrong: Larger batch size can lead to sharper minima and may not reduce overfitting.
Increase the learning rate
Why wrong: Higher learning rate may cause divergence, not reduce overfitting.
Add more features
Why wrong: More features increase model complexity and may worsen overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the amount of training data
Option C is correct. Adding more training data can help reduce overfitting by providing a more representative sample. Option A is wrong because increasing model complexity (more features) would worsen overfitting. Option B is wrong because increasing learning rate may cause instability. Option D is wrong because increasing batch size may not help and could lead to overfitting.
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 amount of training data
Why this is correct
More data helps the model generalize better.
Related concept
Read the scenario before looking for a memorised answer.
Increase the batch size
Why it's wrong here
Larger batch size can lead to sharper minima and may not reduce overfitting.
Increase the learning rate
Why it's wrong here
Higher learning rate may cause divergence, not reduce overfitting.
Add more features
Why it's wrong here
More features increase model complexity and may worsen overfitting.
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 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
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: Increase the amount of training data — Option C is correct. Adding more training data can help reduce overfitting by providing a more representative sample. Option A is wrong because increasing model complexity (more features) would worsen overfitting. Option B is wrong because increasing learning rate may cause instability. Option D is wrong because increasing batch size may not help and could lead to overfitting.
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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