The answer is to increase the dropout rate and implement early stopping. When validation loss increases while training loss continues to drop, the network is memorizing noise in the training data rather than learning generalizable patterns—a classic sign of overfitting. Dropout randomly deactivates a fraction of neurons during each forward pass, forcing the network to learn redundant, robust representations that prevent co-adaptation. Early stopping complements this by monitoring validation loss and halting training the moment it stops improving, directly addressing the symptom of diverging loss curves. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of regularization techniques and training dynamics; a common trap is to continue training longer or add more layers, which worsens overfitting. Remember the mnemonic “Drop and Stop”—dropout to break dependencies, early stopping to cut off runaway learning.
MLS-C01 Modeling Practice Question
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 for image classification. The training loss decreases but validation loss increases after a few epochs. Which TWO actions should be taken to address this?
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 monitors validation loss and halts training when it stops improving, preventing overfitting. This directly addresses the symptom of decreasing training loss with increasing validation loss, which is a classic sign of overfitting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Implement early stopping based on validation loss.
Why this is correct
Early stopping prevents overfitting by halting training when validation loss stops improving.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the learning rate.
Why it's wrong here
Increasing learning rate may cause training to diverge.
✓
Increase the dropout rate.
Why this is correct
Higher dropout regularizes the network and reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of epochs.
Why it's wrong here
More epochs without regularization will likely worsen overfitting.
✗
Add more convolutional layers.
Why it's wrong here
Adding more layers increases model capacity, which may worsen overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between underfitting and overfitting solutions, where candidates mistakenly choose capacity-increasing options (like more layers or epochs) when the problem is overfitting, not underfitting.
Detailed technical explanation
How to think about this question
Overfitting occurs when the model memorizes training data noise instead of learning generalizable patterns. Dropout randomly deactivates neurons during training, acting as a form of regularization that forces the network to learn redundant representations, reducing co-adaptation. Early stopping works by treating the validation loss as a proxy for generalization error and halting training at the point where this error begins to rise, effectively selecting the model with the best generalization performance.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement early stopping based on validation loss. — Option A is correct because early stopping monitors validation loss and halts training when it stops improving, preventing overfitting. This directly addresses the symptom of decreasing training loss with increasing validation loss, which is a classic sign of overfitting.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a neural network for image classification. The training loss is decreasing steadily, but the validation loss starts increasing after a few epochs. What is the MOST likely cause?
easy
A.The learning rate is too high
B.The gradients are vanishing
C.The model is underfitting
✓ D.The model is overfitting to the training data
Why D: The correct answer is D because the validation loss increasing while the training loss continues to decrease is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.
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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.
Question Discussion
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