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.

• ✗

  Detecting important milestones in a project timeline using AI

  Why it's wrong here

  Project milestones are temporal, schedule-based events in a project management plan, not visual patterns present in photographic images. Landmark detection is a computer vision capability that identifies physical places—monuments, buildings, or natural formations—by analyzing pixel patterns; it cannot infer dates, deadlines, or phases of a business timeline. This is a semantic mismatch between project-management terminology and the input/output domain of an image recognition service.

• ✓

  Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score

  Why this is correct

  This is exactly what Azure AI Vision's built-in landmark detection was designed to do. The service takes a photograph, uses deep learning to detect recognizable globally famous locations, and returns the landmark name along with a confidence score indicating how strongly the image matches the known location. It also provides bounding boxes and can handle multiple landmarks in one image, enabling automatic location tagging for travel and photo-management applications.

• ✗

  Creating highlighted markers on maps showing user-defined points of interest

  Why it's wrong here

  Highlighted map markers for user-defined points of interest are a geographic/mapping display feature, typically created with GIS or map SDKs when users specify coordinates—not an AI inference performed on image content. Azure AI Vision's landmark detection works in the opposite direction: it receives an existing photo, identifies known landmarks within it, and outputs labels and confidence values. No markers are drawn on a map, and no user-defined location list is required for the service to operate.

• ✗

  Detecting major architectural features of any building regardless of whether it is famous

  Why it's wrong here

  Azure AI Vision's landmark detection capability matches a photo to a curated catalog of globally recognized named locations, such as the Eiffel Tower or Big Ben, and returns a confidence score for the name. It does not perform general architectural analysis, like identifying arches, columns, or building styles, on arbitrary structures—that task belongs to image captioning or custom vision models. Therefore, detecting major architectural features of any building regardless of fame falls outside landmark detection.

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