MLS-C01 Modeling Practice Question
A data scientist trains a model using SageMaker and notices that the training loss decreases but validation loss increases after a few epochs. What is the MOST likely issue?
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 the training data.
Overfitting occurs when the model performs well on training data but poorly on validation data. Option D is correct. Option A is incorrect because a low learning rate would slow convergence, not cause validation loss to increase while training loss decreases. Option B is incorrect because data leakage would cause both training and validation loss to be artificially low. Option C is incorrect because underfitting would show high training loss.
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 low.
Why it's wrong here
Low learning rate would cause slow convergence, not divergence.
- ✗
There is data leakage from validation to training.
Why it's wrong here
Data leakage would cause both to be artificially low.
- ✗
The model is underfitting the training data.
Why it's wrong here
Underfitting would show high training loss.
- ✓
The model is overfitting the training data.
Why this is correct
Classic sign of overfitting: training loss decreases, validation loss increases.
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