Generative AI Assessment

Evaluate knowledge of AI systems that generate text, images, or code using large language models, diffusion models, prompt engineering, and responsible AI practices.

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Questions 20 Questions

Passing Score 60% Correct

Time Limit 30 Minutes

Format Multiple Choice

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Generative AI Assessment

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Which architecture is most commonly used in modern LLMs?

Which risk is most associated with generative AI?

Which method helps guide structured outputs from LLMs?

What is temperature in text generation?

Which evaluation metric is commonly used for generative text quality?

What does "prompt engineering" aim to improve?

Which task is best suited for generative AI?

What is the purpose of embeddings in generative AI?

Why are diffusion models computationally expensive?

Which practice promotes responsible AI usage?

Which generative model is widely used for image generation?

What is the main benefit of Retrieval-Augmented Generation (RAG)?

What is RLHF used for in LLM training?

What does fine-tuning an LLM involve?

What is the primary function of a Large Language Model (LLM)?

What is a key limitation of LLMs?

What differentiates diffusion models from GANs?

What does "top-p" (nucleus) sampling control?

What is a token in LLMs?

Which technique helps reduce prompt injection attacks?

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