AI0-001 Machine Learning and Deep Learning Practice Question
Exhibit
Refer to the exhibit. Training log: Epoch 1/20 loss: 1.2 - acc: 0.45 - val_loss: 1.3 - val_acc: 0.42 Epoch 5/20 loss: 0.4 - acc: 0.85 - val_loss: 1.1 - val_acc: 0.68 Epoch 10/20 loss: 0.1 - acc: 0.98 - val_loss: 2.1 - val_acc: 0.60
Based on the exhibit, what is the likely problem with the model?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting by showing loss curves where candidates mistakenly focus on the low training loss alone, ignoring the rising validation loss that confirms 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 exhibit shows training loss decreasing to near zero while validation loss increases after a certain point, which is a classic sign of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch size too small
Why it's wrong here
Small batch size introduces noise but not necessarily this pattern.
- ✓
Overfitting
Why this is correct
Correct: Training loss decreases but validation loss increases, classic overfitting.
- ✗
Learning rate too high
Why it's wrong here
A high learning rate would cause loss to diverge or oscillate.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high training loss, not low.
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.