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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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