MLS-C01 Modeling Practice Question

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?

⚠ Common exam trap

AWS often tests the distinction between overfitting and underfitting by describing a scenario where training loss decreases but validation loss increases, and the trap is that candidates may mistakenly attribute this to a high learning rate or vanishing gradients instead of recognizing it as the hallmark of overfitting.

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

✓

The model is overfitting to the training data

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.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The learning rate is too high
Why it's wrong here
High learning rate would cause training loss to fluctuate or not converge.
✗
The gradients are vanishing
Why it's wrong here
Vanishing gradients cause training loss to plateau.
✗
The model is underfitting
Why it's wrong here
Underfitting would show high training loss.
✓
The model is overfitting to the training data
Why this is correct
Overfitting causes validation loss to increase.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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