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AIF-C01 practice question: Generative AI concepts (2 of 3)

Fundamentals of Generative AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A furniture retailer wants to create photo-realistic images of sofas in different room settings from short text descriptions, without photographing every combination.

Which type of generative model is BEST known for this kind of text-to-image generation?

Choose one answer.

  1. A. A regression model trained on past sofa prices to estimate what each sofa should cost.
  2. B. A clustering model that groups similar room photos into style families for easy browsing.
  3. C. A diffusion model that refines random noise into a new image, guided by the text prompt.
  4. D. A speech recognition model that converts spoken product descriptions into written text.
Show the answer and explanation

Answer: C

  • A. Wrong: Regression predicts a numeric value, such as a price. It does not create images from text descriptions.
  • B. Wrong: Clustering sorts existing photos into groups. It cannot produce a new image of a sofa that was never photographed.
  • C. Right: Correct. Diffusion models generate images by removing noise step by step, guided by the text prompt. This is the approach behind most modern text-to-image tools. Learn more: Amazon Titan Image Generator G1 model (opens in a new tab)
  • D. Wrong: Speech recognition converts audio into text. It works in the opposite direction from generating an image from a description.

Explanation

Text-to-image generation is a generative task, and diffusion models are the standard approach for it. The retailer needs new images created from descriptions, not predictions or sorting of existing photos. This question tests recognizing which model type generates images.

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