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 convolutional neural network (CNN) for image classification using Amazon SageMaker. The training loss decreases steadily but validation loss starts increasing after a few epochs. Which action should the data scientist take to address this issue?
Increase the learning rate
Why wrong: Higher learning rate can cause divergence and overfitting.
Add more convolutional layers
Why wrong: More layers increase model capacity and overfitting.
Increase the batch size
Why wrong: Larger batch size can lead to sharp minima and overfitting.
Implement early stopping based on validation loss
Early stopping prevents overfitting by stopping training when validation loss plateaus or increases.
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
The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or starts to increase), preventing the model from memorizing noise in the training data. In SageMaker, this can be implemented using the `EarlyStopping` callback in the framework's estimator or by setting `use_early_stopping` to True in a built-in algorithm.
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 learning rate
Why it's wrong here
Higher learning rate can cause divergence and overfitting.
Add more convolutional layers
Why it's wrong here
More layers increase model capacity and overfitting.
Increase the batch size
Why it's wrong here
Larger batch size can lead to sharp minima and overfitting.
Implement early stopping based on validation loss
Why this is correct
Early stopping prevents overfitting by stopping training when validation loss plateaus or increases.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse overfitting with underfitting and choose to increase model complexity (Option B) or learning rate (Option A), not recognizing that rising validation loss signals the need to stop training rather than continue with more capacity.
Detailed technical explanation
Early stopping works by saving the model weights at the epoch with the lowest validation loss (e.g., using `model_checkpoint` in SageMaker's TensorFlow or PyTorch estimators). A patience parameter (commonly 5–10 epochs) allows the validation loss to fluctuate without prematurely stopping, which is critical because validation loss can sometimes plateau before dropping again. In practice, early stopping is often combined with learning rate reduction (e.g., ReduceLROnPlateau) to further stabilize training.
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.
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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 — The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or starts to increase), preventing the model from memorizing noise in the training data. In SageMaker, this can be implemented using the `EarlyStopping` callback in the framework's estimator or by setting `use_early_stopping` to True in a built-in algorithm.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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