MLS-C01 Modeling Practice Question
A data scientist is training a neural network for a multi-class classification problem with 100 classes. The model uses a softmax output layer and cross-entropy loss. During training, the loss decreases steadily but the accuracy on the validation set plateaus early. Which of the following is the most likely cause?
⚠ Common exam trap
AWS often tests the distinction between overfitting and underfitting by pairing a decreasing training loss with a plateauing validation metric, tricking candidates into choosing learning rate or epoch issues when the real problem is memorization.
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
When the validation accuracy plateaus early while training loss continues to decrease, it indicates that the model is memorizing the training data rather than learning generalizable patterns. This is classic overfitting, where the softmax output layer produces high-confidence predictions for training samples but fails to generalize to unseen validation data, causing cross-entropy loss to drop on the training set while validation accuracy stagnates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch size is too large
Why it's wrong here
Large batch size can lead to poor generalization but the loss would still decrease; validation accuracy might be lower but not necessarily plateau.
- ✓
The model is overfitting the training data
Why this is correct
Overfitting occurs when the model learns training data noise, causing training loss to keep decreasing while validation performance stagnates.
- ✗
Number of epochs is too small
Why it's wrong here
Too few epochs would show underfitting (both training and validation accuracy low), not a plateau after decrease.
- ✗
Learning rate is too high
Why it's wrong here
A high learning rate would cause the loss to oscillate or diverge, not steadily decrease.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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