Question 926 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image segmentation using a U-Net architecture. The model overfits severely. The scientist tries L2 regularization, dropout, and data augmentation, but validation loss remains high while training loss approaches zero. Which additional strategy is most likely to reduce overfitting?
⚠ Common exam trap
AWS often tests the misconception that increasing regularization (L2, dropout, augmentation) is always sufficient, but the trap here is that when those techniques fail, early stopping is the next logical step because it directly stops the overfitting process at the optimal point, whereas the other options either increase capacity or destabilize training.
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, directly addressing overfitting by preventing the model from memorizing noise after it has learned generalizable features. Since the training loss is near zero but validation loss remains high, the model has already started overfitting, and early stopping can cut training at the point just before overfitting worsens.
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 stopping training before the model starts to memorize the training data.
- ✗
Increase the batch size
Why it's wrong here
Larger batch sizes can lead to sharper minima and may not reduce overfitting.
- ✗
Use a larger learning rate
Why it's wrong here
A larger learning rate can cause divergence and does not specifically address overfitting.
- ✗
Add more convolutional layers to increase model capacity
Why it's wrong here
Increasing model capacity typically leads to more overfitting.
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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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