Question 1,737 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on a GPU instance. The training loss is decreasing, but the validation loss starts increasing after a few epochs. Which action should the data scientist take to address this?
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
Early stopping monitors validation loss and stops training when it starts to increase, which directly addresses overfitting. Option A (reduce batch size) is not the best action; while it can affect training dynamics, it does not directly prevent validation loss increase due to overfitting. Option C (increase learning rate) is incorrect as it may cause the model to diverge or overshoot optimal minima. Option D (add more layers) is incorrect because adding layers increases model complexity, which typically worsens 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.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch size may increase noise.
- ✓
Implement early stopping
Why this is correct
Early stopping halts training when validation loss increases.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate may cause divergence.
- ✗
Add more layers to the model
Why it's wrong here
More layers can increase overfitting.
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Last reviewed: Jun 20, 2026
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