Courseiva
AI Models and Data EngineeringhardMultiple ChoiceObjective-mapped

AI0-001 AI Models and Data Engineering Practice Question

Exhibit

Refer to the exhibit.

```
Epoch 1/10 - loss: 0.6932 - accuracy: 0.5234 - val_loss: 0.6918 - val_accuracy: 0.5312
Epoch 2/10 - loss: 0.4231 - accuracy: 0.8047 - val_loss: 0.5234 - val_accuracy: 0.7422
Epoch 3/10 - loss: 0.3125 - accuracy: 0.8828 - val_loss: 0.6015 - val_accuracy: 0.7344
Epoch 4/10 - loss: 0.2146 - accuracy: 0.9219 - val_loss: 0.7234 - val_accuracy: 0.7188
Epoch 5/10 - loss: 0.1478 - accuracy: 0.9531 - val_loss: 0.8342 - val_accuracy: 0.7031
```

An engineer is training a neural network and observes the output shown. Which conclusion is most likely correct?

⚠ Common exam trap

CompTIA often tests the distinction between overfitting and underfitting by presenting a loss curve where training loss decreases but validation loss increases, leading candidates to mistakenly attribute the issue to vanishing gradients or a high learning rate.

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 after epoch 2.

The output shows training loss decreasing while validation loss increases after epoch 2, which is a classic sign of overfitting. The model begins to memorize the training data rather than generalize, leading to poor performance on unseen data. This pattern confirms that overfitting starts after epoch 2, making option B correct.

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 gradients are vanishing.

    Why it's wrong here

    Gradients vanishing would cause slow convergence.

  • The model is overfitting after epoch 2.

    Why this is correct

    Training loss decreases, validation loss increases.

  • The model is underfitting.

    Why it's wrong here

    Training accuracy is high, so not underfitting.

  • The learning rate is too high.

    Why it's wrong here

    Loss is not diverging.

About these practice questions

This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.