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?
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
If the generator loss drops too fast and discriminator loss rises, the generator is overpowering the discriminator. The typical remedy is to train the discriminator more often (e.g., 5 steps per generator step) or adjust the learning rates.
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
Mode collapse leads to limited variety, not necessarily the described loss pattern.
- ✗
The discriminator is overfitting; decrease its capacity
Why it's wrong here
Overfitting would cause discriminator loss to be low, not high.
- ✗
The learning rates are too high; reduce both
Why it's wrong here
High learning rates would cause both losses to oscillate, not this specific pattern.
- ✓
The generator is too strong; train the discriminator more frequently
Why this is correct
Training the discriminator more often helps it catch up to the generator, balancing the GAN.
About these practice questions
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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.