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AI0-001 Machine Learning and Deep Learning Practice Question

Exhibit

Refer to the exhibit.

```
Epoch 1/10
 - loss: 0.6932 - acc: 0.5123 - val_loss: 0.6981 - val_acc: 0.5012
Epoch 2/10
 - loss: 0.4521 - acc: 0.7845 - val_loss: 0.6890 - val_acc: 0.5123
Epoch 3/10
 - loss: 0.2312 - acc: 0.9234 - val_loss: 0.7123 - val_acc: 0.4987
Epoch 4/10
 - loss: 0.1023 - acc: 0.9789 - val_loss: 0.8567 - val_acc: 0.4856
Epoch 5/10
 - loss: 0.0456 - acc: 0.9923 - val_loss: 1.0234 - val_acc: 0.4765
```

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

⚠ Common exam trap

CompTIA often tests the distinction between overfitting and underfitting by showing a training log where training accuracy is high but validation accuracy is low, leading candidates to mistakenly think the model is underfitting because validation performance is poor.

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

Overfitting

The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

Answer analysis

Option-by-option breakdown

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

  • Underfitting

    Why it's wrong here

    Underfitting would show high loss on both training and validation sets.

  • Data leakage

    Why it's wrong here

    Data leakage would cause overly optimistic performance, not divergence.

  • Overfitting

    Why this is correct

    Training loss decreases while validation loss increases, a classic sign of overfitting.

  • Vanishing gradient

    Why it's wrong here

    No evidence of gradients vanishing; loss is decreasing on training.

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