Quick Answer
The correct answer is to implement early stopping based on validation loss. This technique directly addresses the classic symptom of overfitting: the training loss continues to drop while the validation loss begins to rise, indicating the model is memorizing noise rather than learning generalizable patterns. By monitoring validation loss and halting training the moment it stops improving, you preserve the model at its optimal generalization point. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of regularization strategies in SageMaker; a common trap is confusing early stopping with other hyperparameter tweaks like increasing the learning rate, which would actually destabilize training further. Remember, when you see the validation loss curve start to climb while training loss falls, think “stop the clock”—early stopping is your first line of defense against overfitting.
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 team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?
Reduce the batch size
Why wrong: Reducing batch size does not prevent overfitting.
Add more convolutional layers
Why wrong: Adding layers increases complexity and may worsen overfitting.
Increase the learning rate
Why wrong: Increasing learning rate may cause divergence.
Implement early stopping based on validation loss
Early stopping prevents 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 based on validation loss
Option A is correct because early stopping prevents overfitting by stopping when validation loss increases. Option B is wrong because increasing learning rate may worsen overfitting. Option C is wrong because adding more layers increases model complexity. Option D is wrong because reducing batch size may slow training but not prevent 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.
Reduce the batch size
Why it's wrong here
Reducing batch size does not prevent overfitting.
Add more convolutional layers
Why it's wrong here
Adding layers increases complexity and may worsen overfitting.
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause divergence.
Implement early stopping based on validation loss
Why this is correct
Early stopping prevents overfitting.
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 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 based on validation loss — Option A is correct because early stopping prevents overfitting by stopping when validation loss increases. Option B is wrong because increasing learning rate may worsen overfitting. Option C is wrong because adding more layers increases model complexity. Option D is wrong because reducing batch size may slow training but not prevent 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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