Fundamentals of Generative AI →hardMultiple ChoiceObjective-mapped

Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

Refer to the exhibit.
```
INFO: Training started.
INFO: Epoch 1/10 - loss: 2.3456 - accuracy: 0.1234
INFO: Epoch 2/10 - loss: 1.9876 - accuracy: 0.2345
INFO: Epoch 3/10 - loss: 1.6543 - accuracy: 0.3456
INFO: Epoch 4/10 - loss: 1.4321 - accuracy: 0.4567
INFO: Epoch 5/10 - loss: 1.2345 - accuracy: 0.5678
INFO: Epoch 6/10 - loss: 1.0987 - accuracy: 0.6789
INFO: Epoch 7/10 - loss: 0.9876 - accuracy: 0.7890
INFO: Epoch 8/10 - loss: 0.8765 - accuracy: 0.8901
INFO: Epoch 9/10 - loss: 0.7654 - accuracy: 0.9012
INFO: Epoch 10/10 - loss: 0.6543 - accuracy: 0.9123
```

Refer to the exhibit. A data scientist is fine-tuning a model. The training loss and accuracy are improving each epoch. However, after training, the model performs poorly on a held-out validation set. What is the most likely issue?

⚠ Common exam trap

Google Cloud often tests the distinction between overfitting and underfitting by presenting improving training metrics alongside poor validation performance, which candidates may misinterpret as a learning rate issue or data leakage if they do not recognize the hallmark divergence pattern.

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 model's training loss and accuracy improve each epoch, but performance on the validation set is poor. This classic symptom indicates overfitting, where the model memorizes the training data (including noise) rather than learning generalizable patterns. In fine-tuning, this often occurs when the model is trained for too many epochs or the dataset is too small relative to model capacity.

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 poor training performance as well.
✗
Inappropriate learning rate
Why it's wrong here
An inappropriate learning rate would typically cause instability or poor convergence, not this pattern.
✗
Data leakage
Why it's wrong here
Data leakage could cause overly optimistic validation scores, but here validation is poor.
✓
Overfitting
Why this is correct
Overfitting leads to good training performance but poor validation.

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Generative AI Leader Fundamentals of Generative AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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