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
Batch size affects gradient noise and convergence stability, not the widening gap between falling training loss and rising validation loss shown. It is tempting because small batches add regularisation-like noise, and reducing batch size would be right when generalisation needs improving without changing architecture.
- ✓
Overfitting
Why this is correct
The exhibit shows training performance continuing to improve while validation performance degrades, the classic divergence indicating the model memorises training noise rather than generalising. That gap between training and validation error is the defining signature of overfitting.
- ✗
Learning rate too high
Why it's wrong here
A high learning rate makes training loss oscillate or diverge rather than plateau; the exhibit shows the flat, high training and validation loss characteristic of underfitting. It is tempting because learning rate is a common tuning suspect, and raising it would be right when convergence is too slow.
- ✗
Underfitting
Why it's wrong here
Underfitting describes high training loss, but the exhibit indicates the model has memorised training data while validation loss rises, which is overfitting. It is tempting because both are generalisation failures, and addressing underfitting would be correct when the model is too simple for the data.
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.