MLS-C01 Modeling Practice Question
Exhibit
Refer to the exhibit. [2019-10-15 14:33:12.123] [INFO] [stdout] [100/100] Epoch: 0, Train Loss: 0.69, Validation Loss: 0.68 [2019-10-15 14:33:12.124] [INFO] [stdout] [100/100] Epoch: 1, Train Loss: 0.68, Validation Loss: 0.68 [2019-10-15 14:33:12.125] [INFO] [stdout] [100/100] Epoch: 2, Train Loss: 0.66, Validation Loss: 0.67 [2019-10-15 14:33:12.126] [INFO] [stdout] [100/100] Epoch: 3, Train Loss: 0.65, Validation Loss: 0.68 [2019-10-15 14:33:12.127] [INFO] [stdout] [100/100] Epoch: 4, Train Loss: 0.63, Validation Loss: 0.67
A data scientist is reviewing the training logs from a SageMaker training job. The logs show training and validation loss per epoch. Based on the exhibited logs, which statement is correct?
⚠ Common exam trap
The trap here is that candidates see decreasing training loss and assume the model is learning well, ignoring the validation loss plateau or increase, which is 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 because training loss decreases while validation loss does not
The training logs show a classic overfitting pattern: training loss consistently decreases across epochs, indicating the model is memorizing the training data, while validation loss does not decrease (or may even increase), indicating poor generalization to unseen data. In SageMaker, monitoring both losses during training is critical to detect overfitting early, often prompting regularization or early stopping.
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 model is not learning because the loss is not decreasing
Why it's wrong here
Training loss is decreasing, so the model is learning.
- ✗
The model is underfitting because both losses are high
Why it's wrong here
Training loss is decreasing, so not underfitting.
- ✗
The model is performing well because validation loss is stable
Why it's wrong here
Stable validation loss that does not decrease indicates the model is not learning the validation set.
- ✓
The model is overfitting because training loss decreases while validation loss does not
Why this is correct
Classic overfitting: training loss improves, validation loss stagnates.
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