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.

• ✗

  Image Analysis (descriptions and tags)

  Why it's wrong here

  Image Analysis for descriptions and tags generates human-readable captions, relevant tags, and category classifications for the overall scene. While it can detect objects and colors, it does not perform character-level text extraction; therefore it cannot read the specific strings on a label, and its output is high-level scene understanding rather than precise alphanumeric data.

  When this WOULD be correct

  A question asking for a service that generates human-readable descriptions of image content, such as 'a man holding a package' or tags like 'outdoor', 'shipping', would make Image Analysis the correct answer.

• ✓

  Optical Character Recognition (OCR)

  Why this is correct

  Azure AI Vision's OCR (via the Read API) is specifically built to extract printed and handwritten text from images, returning both the text string and its bounding-box coordinates. For shipping labels, this is exactly what is needed to capture addresses, tracking numbers, and other alphanumeric content accurately, even under uneven lighting or slight rotation.

• ✗

  Object Detection

  Why it's wrong here

  Object Detection in Azure AI Vision is designed to locate instances of objects within an image by drawing bounding boxes and assigning class labels (e.g., 'box', 'pallet'). It does not perform optical character recognition, so it cannot read or transcribe the text printed on the label; it only tells you that an object is present and where.

• ✗

  Face API

  Why it's wrong here

  The Face API performs face detection (facial landmarks), verification, identification, and emotion analysis on human faces. It has no capability to extract text characters or understand the contents of shipping labels, as those contain addresses and codes, not facial features; using it for text extraction would be semantically and technically incorrect.

  When this WOULD be correct

  A question asking for a solution to verify the identity of package recipients by matching their face against a database of authorized recipients would make Face API the correct answer.

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?”

Go deeper

Related to this question