AI-900 Practice Question: Describe features of computer vision workloads on Azure

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Azure AI Custom Vision

  Why it's wrong here

  Azure AI Custom Vision is a model-training service that requires users to upload their own labeled images and run a custom training pipeline to create a domain-specific image classifier or object detector. Although it can technically generate tags, those tags are defined by the user's training data, so the museum would need to manually annotate a large dataset of exhibit images first. The question explicitly states no custom training is desired, and the prebuilt Computer Vision service already provides general tagging without that overhead.

  When this WOULD be correct

  A museum wants to train a model to recognize unique artistic styles specific to its collection, such as 'Van Gogh's brushstrokes' or 'Renaissance composition,' and has labeled images. Custom Vision would be correct for this custom classification task.

• ✓

  Azure AI Computer Vision Image Analysis

  Why this is correct

  The prebuilt Image Analysis API in Azure AI Computer Vision uses transformer-based models trained on massive image datasets to extract rich semantic metadata. It automatically generates descriptive tags for objects, scenes, actions, and abstract concepts, and it also produces human-readable captions and detects brands, landmarks, and image types. This service requires zero custom training or labeled data from the museum, making it the only listed option that directly addresses the need to auto-generate descriptive tags from artwork and exhibits without building a custom solution.

• ✗

  Azure AI Face service

  Why it's wrong here

  Azure AI Face is a specialized service that focuses exclusively on detecting, identifying, and analyzing human faces — including attributes like age, emotion, facial hair, and landmarks. It cannot generate descriptive tags for arbitrary images, artwork, or museum exhibits because it expects a human face as the primary input and does not perform general scene understanding, object detection, or label generation. Using Face would fail for most museum content, such as landscapes, sculptures, or abstract paintings.

  When this WOULD be correct

  A question asking for a service to detect faces in images, verify identities, or find similar faces in a collection would make Face the correct answer, especially if the scenario involves security or personalization.

• ✗

  Azure AI Form Recognizer

  Why it's wrong here

  Azure AI Form Recognizer (now Azure AI Document Intelligence) is built for document automation: it extracts text, tables, checkboxes, and key-value pairs from structured or semi-structured documents such as invoices, receipts, tax forms, and contracts. It does not perform image understanding, object detection, or semantic tagging of visual content; it only interprets the textual and layout structure within scanned documents. Therefore it cannot generate tags describing an image's content, only what is written in the document.

  When this WOULD be correct

  A question asking: 'A company needs to automatically extract key-value pairs and table data from scanned invoices. Which Azure AI service should they use?' would make Form Recognizer the correct answer, as it specializes in document understanding and extraction.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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