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 neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?
Implement early stopping
Early stopping prevents overfitting by halting training when validation loss increases.
Add more layers to the network
Why wrong: Adding more layers could increase overfitting.
Reduce the learning rate
Why wrong: Reducing learning rate may help but early stopping is more direct.
Increase the number of epochs
Why wrong: Increasing epochs would 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
Implement early stopping
Increasing validation loss indicates overfitting. Early stopping halts training when validation loss stops improving, preventing overfitting. Increasing epochs would worsen overfitting. Reducing learning rate might help but early stopping directly addresses the issue. Adding more layers could increase overfitting. Option A: Early stopping is correct. Option B: Increasing epochs would worsen overfitting. Option C: Reducing learning rate might help but not as directly. Option D: Adding more layers could increase 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.
Implement early stopping
Why this is correct
Early stopping prevents overfitting by halting training when validation loss increases.
Related concept
Read the scenario before looking for a memorised answer.
Add more layers to the network
Why it's wrong here
Adding more layers could increase overfitting.
Reduce the learning rate
Why it's wrong here
Reducing learning rate may help but early stopping is more direct.
Increase the number of epochs
Why it's wrong here
Increasing epochs would 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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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 — Increasing validation loss indicates overfitting. Early stopping halts training when validation loss stops improving, preventing overfitting. Increasing epochs would worsen overfitting. Reducing learning rate might help but early stopping directly addresses the issue. Adding more layers could increase overfitting. Option A: Early stopping is correct. Option B: Increasing epochs would worsen overfitting. Option C: Reducing learning rate might help but not as directly. Option D: Adding more layers could increase 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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