AI-901 - Implement AI solutions by using Microsoft Foundry - Section 2.3

Implement AI solutions with computer vision and image-generation capabilities by using Foundry, including interpreting visual input and creating visual outputs.

Interpreting visual input in prompts by using a deployed multimodal model; creating new visual outputs by using generative image models; and building a lightweight application that includes vision capabilities. The single-best decision is between a multimodal model that interprets an existing image and a generative image model that produces a new one.

multimodal modelinterpret visual inputimage-generation modelsgenerate visual outputsvision application

Practice question for this objective

Free sampleImplement AI solutions by using Microsoft Foundrymedium

A logistics firm is comparing two tasks: task one sends a scanned delivery photo to a model and asks "is the parcel damaged?", while task two asks a model to create a new promotional picture of an undamaged parcel from a written brief. Which statement correctly distinguishes the model capabilities these two tasks require?

  • ATask one needs a multimodal model to interpret the photo, and task two needs an image-generation model to create the promotional picture Correct
  • BBoth tasks require an image-generation model, because both involve pictures
  • CBoth tasks require a multimodal model, because it can handle any picture-related workload
  • DTask one needs an embedding model and task two needs a language model
Interpreting visual input needs a multimodal model, while creating pictures needs an image-generation model. The direction of the visual data decides the capability. When an image goes in and an answer comes out, that is multimodal interpretation; when text goes in and a new picture comes out, that is image generation. The two are not interchangeable.

Why A is correct: Task one supplies an image plus a question and expects reasoning over the visual input, which is multimodal interpretation, while task two turns a text brief into a new picture, which is image generation, so each task maps to a different capability.

Why B is wrong: Sharing the theme of pictures does not make the capabilities identical; task one reads an existing image and must interpret it, which an image-generation model cannot do.

Why C is wrong: A multimodal model interprets visual input but does not synthesise new original pictures, so it cannot satisfy task two despite handling task one.

Why D is wrong: An embedding model only vectorises input and cannot answer whether a parcel is damaged, and a language model outputs text rather than the picture task two needs, so both mappings are wrong.

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