Fundamentals of Generative AI II
Explore foundational concepts of generative AI including transformer architectures and diffusion models. Understand the use of AWS AI services for practical applications in data analysis and image generation. Learn to identify toxic content and hallucinations in AI responses and discover how to enhance foundation models for specific tasks. This lesson helps you build core knowledge to tackle AI and machine learning questions effectively for the AWS Certified AI Practitioner exam.
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Question 19
Which statement best describes the difference between transformers and Stable Diffusion models?
A. Transformers are a type of neural network architecture used for sequential data, primarily in natural language processing tasks, while Stable Diffusion models are designed specifically for generating high-quality images through denoising processes.
B. Transformers are used exclusively for text-based tasks, whereas Stable Diffusion models are a type of transformer architecture adapted for image generation.
C. Transformers are neural networks designed for solving computer vision problems, while Stable Diffusion models focus exclusively on generative tasks in natural language processing.
D. Transformers and Stable Diffusion models are unrelated; transformers process structured data only, while Stable Diffusion models are built for unstructured image data.
Question 20
A retail company is seeking to empower its business analysts by providing them with a tool that ...