This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 training a regression model. The training loss is decreasing but the validation loss starts to increase after a few epochs. Which technique should the scientist use to address this issue?
Decrease the batch size.
Why wrong: Smaller batch size may help generalization but not directly stop overfitting.
Implement early stopping based on validation loss.
Stops training before overfitting.
Add more layers to the model.
Why wrong: More layers increase capacity and risk overfitting.
Increase the learning rate.
Why wrong: Higher learning rate may cause instability.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Implement early stopping based on validation loss.
Option B is correct because early stopping halts training when validation loss stops improving, preventing overfitting. Option A is wrong because adding more layers may worsen overfitting. Option C is wrong because increasing learning rate can cause divergence. Option D is wrong because decreasing batch size may increase noise but not directly address 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.
Decrease the batch size.
Why it's wrong here
Smaller batch size may help generalization but not directly stop overfitting.
Implement early stopping based on validation loss.
Why this is correct
Stops training before overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Add more layers to the model.
Why it's wrong here
More layers increase capacity and risk overfitting.
Increase the learning rate.
Why it's wrong here
Higher learning rate may cause instability.
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: Implement early stopping based on validation loss. — Option B is correct because early stopping halts training when validation loss stops improving, preventing overfitting. Option A is wrong because adding more layers may worsen overfitting. Option C is wrong because increasing learning rate can cause divergence. Option D is wrong because decreasing batch size may increase noise but not directly address 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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