AI0-001 AI Concepts and Techniques Practice Question
A team is training a generative adversarial network (GAN) to generate realistic images of furniture. The generator loss decreases sharply while the discriminator loss increases. What is the MOST likely issue and recommended action?
⚠ Common exam trap
Many exam-takers confuse generator dominance with mode collapse; candidates may pick mode collapse because it is a well-known GAN failure, but the described loss pattern points to discriminator weakness.
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 generator is too strong; train the discriminator more frequently
When the generator loss decreases sharply while the discriminator loss increases, the generator is producing samples realistic enough to fool the discriminator consistently. This indicates the discriminator is not learning effectively, so the recommended action is to strengthen the discriminator by training it more frequently (or increasing its capacity). Training the discriminator more frequently gives it more opportunities to distinguish real from fake, restoring balance in the adversarial game.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mode collapse has occurred; increase the generator's learning rate
Why it's wrong here
Rising discriminator loss with falling generator loss signals discriminator failure, not mode collapse; mode collapse shows low generator diversity with the discriminator still separating fakes. Raising the generator learning rate worsens the imbalance. Mode collapse detection would be the right focus if the generator produced identical outputs.
- ✗
The discriminator is overfitting; decrease its capacity
Why it's wrong here
Overfitting would show the discriminator performing well on training data but poorly on held-out samples, not a steadily climbing loss. Here the discriminator is being overwhelmed as the generator improves. Reducing discriminator capacity would be correct if validation metrics diverged from training metrics.
- ✗
The learning rates are too high; reduce both
Why it's wrong here
Excessive learning rates produce oscillating or diverging losses on both networks, not one falling while the other climbs. The pattern here reflects an overpowering generator. Reducing both rates would be the right response if both losses spiked or failed to converge.
- ✓
The generator is too strong; train the discriminator more frequently
Why this is correct
When generator loss falls while discriminator loss rises, the discriminator can no longer distinguish real from fake, so the generator dominates. Training the discriminator more frequently restores adversarial balance, giving it enough updates to keep pace and prevent mode collapse.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.