AI0-001 AI Concepts and Foundations Practice Question
Exhibit
Training log: Epoch 1/10 - loss: 0.6932 - accuracy: 0.5023 - val_loss: 0.6941 - val_acc: 0.5001 Epoch 2/10 - loss: 0.6810 - accuracy: 0.5432 - val_loss: 0.7123 - val_acc: 0.4987 Epoch 3/10 - loss: 0.6645 - accuracy: 0.5876 - val_loss: 0.7356 - val_acc: 0.4953 ... Epoch 10/10 - loss: 0.6234 - accuracy: 0.6521 - val_loss: 0.8123 - val_acc: 0.4889
Refer to the exhibit. The training log shows loss and accuracy for a binary classification model. What is the most likely issue with this model?
⚠ Common exam trap
The key trap is that candidates may only look at the final accuracy numbers without comparing training and validation curves, missing the divergence that indicates 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
✓
Overfitting
The training log shows that training loss continues to decrease while validation loss increases after a certain point, and training accuracy approaches 100% while validation accuracy plateaus or drops. This divergence is the classic signature of overfitting, where the model memorizes noise in the training data 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.
- ✓
Overfitting
Why this is correct
Overfitting is indicated when training loss keeps falling and training accuracy approaches 100% while validation loss rises and validation accuracy stagnates or degrades. That divergence between training and validation curves is the classic signature, matching the exhibit's logged behaviour.
- ✗
Insufficient epochs
Why it's wrong here
Insufficient epochs would leave both training and validation loss still decreasing together; the exhibit shows training loss continuing to fall while validation loss climbs, indicating overfitting rather than premature stopping. It is tempting because stopping training early is a common cause of a model that has not yet converged.
- ✗
Underfitting
Why it's wrong here
Underfitting would show both training and validation loss remaining high and accuracy plateauing near chance; the exhibit instead shows training loss falling while validation loss rises, which is overfitting. It is tempting because underfitting is the classic diagnosis when a model's accuracy stalls at a poor level.
- ✗
Data leakage
Why it's wrong here
Data leakage would inflate validation accuracy while training loss falls normally, not produce the exhibit's diverging curves. It is tempting because leakage genuinely causes suspiciously high scores, and it would be the answer if training accuracy were near-perfect while validation accuracy stayed near chance.
About these practice questions
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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.