2026

Artificial Intelligence & Machine Learning

Generative AI Hard Quiz

Test your mastery of cutting‑edge generative AI concepts with ten challenging multiple‑choice items covering models, training tricks, and evaluation metrics.

#deep learning#prompt engineering#transformers#neural networks#generative models#model evaluation#creative AI#diffusion#text synthesis#AI art

Quiz Intelligence

Total Questions

10

Difficulty Level

hard

Progress

0/0

0%

Quiz Questions

Answer all questions below and test your knowledge.

1
Which architecture introduced the concept of self‑attention that underpins most modern generative text systems?
Question 1
2
In diffusion models, what operation replaces the traditional decoder during the generation phase?
Question 2
3
What metric quantifies the divergence between the distribution of generated images and the real‑world image distribution in GAN evaluation?
Question 3
4
Which loss function is commonly paired with a generator to encourage diversity in its outputs?
Question 4
5
When scaling a language model from 1 B to 100 B parameters, which factor typically dominates the increase in inference latency?
Question 5
6
Which technique allows a diffusion model to produce high‑resolution images without training a separate large‑scale network?
Question 6
7
In prompt engineering for image generation, what term describes the practice of adding negative descriptors to suppress unwanted attributes?
Question 7
8
What theoretical bound explains why larger transformer models exhibit emergent abilities after a certain scale?
Question 8
9
Which dataset introduced the benchmark for text‑to‑image generation that includes 4 million captioned images?
Question 9
10
When fine‑tuning a pretrained diffusion model on a niche domain, which parameter is most efficient to adjust for quick adaptation?
Question 10

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Generative AI Hard Quiz

Quiz Intelligence

Quiz Questions

Which architecture introduced the concept of self‑attention that underpins most modern generative text systems?

In diffusion models, what operation replaces the traditional decoder during the generation phase?

What metric quantifies the divergence between the distribution of generated images and the real‑world image distribution in GAN evaluation?

Which loss function is commonly paired with a generator to encourage diversity in its outputs?

When scaling a language model from 1 B to 100 B parameters, which factor typically dominates the increase in inference latency?

Which technique allows a diffusion model to produce high‑resolution images without training a separate large‑scale network?

In prompt engineering for image generation, what term describes the practice of adding negative descriptors to suppress unwanted attributes?

What theoretical bound explains why larger transformer models exhibit emergent abilities after a certain scale?

Which dataset introduced the benchmark for text‑to‑image generation that includes 4 million captioned images?

When fine‑tuning a pretrained diffusion model on a niche domain, which parameter is most efficient to adjust for quick adaptation?