NCA-GENL Core Machine Learning and AI Knowledge Practice Question
In the context of generative AI, what is the 'mode collapse' problem in GANs, and why is it a significant challenge?
⚠ Common exam trap
Candidates often confuse mode collapse with vanishing gradients or training divergence, failing to realize it specifically refers to the generator's inability to produce diverse outputs despite the discriminator's feedback.
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 fails to produce diverse samples.
Mode collapse occurs when the generator in a Generative Adversarial Network learns to map several input noise vectors to the same output or a very limited set of outputs. This prevents the generator from capturing the full diversity of the target data distribution. It is a major challenge because it defeats the purpose of generative modeling, which is to produce a wide range of realistic, diverse samples that represent the training distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The generator output becomes purely random noise.
Why it's wrong here
Purely random noise would indicate that the generator is failing to learn any structure. Mode collapse is different; it is a failure to represent diversity. In mode collapse, the generator finds a single output that successfully fools the discriminator, so it stops exploring other parts of the data space.
- ✓
The generator fails to produce diverse samples.
Why this is correct
Mode collapse occurs when the generator produces only a limited subset of the actual data distribution. Because the generator finds one output that satisfies the discriminator, it stops learning the full variety of the data, resulting in highly repetitive outputs that fail to capture the complexity of the training data.
- ✗
The discriminator becomes too powerful to train.
Why it's wrong here
If the discriminator is too powerful, the generator might suffer from vanishing gradients, not mode collapse. Mode collapse is specifically about the generator finding a single 'winning' sample that tricks the discriminator, rather than the discriminator being unable to be trained at all. They are distinct, known GAN training issues.
- ✗
Training loss increases monotonically to infinity.
Why it's wrong here
Mode collapse is characterized by the generator producing repetitive outputs, not necessarily by exploding loss values. While training GANs is notoriously unstable, the loss metrics in mode collapse often show the generator 'succeeding' against the discriminator for a specific, narrow range of outputs, rather than a system-wide catastrophic failure of loss calculation.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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