Question 754 of 1,672
Fixing Neural Network Overfitting with Early Stopping and Dropout
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?
Quick Answer
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.
⚠ Common exam trap
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.
Answer choices
Why each option matters
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.
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.
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.
- ✗
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.
- ✗
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.
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Same concept, more angles
1 more way this is tested on MLS-C01
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 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.
Last reviewed: Jun 30, 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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