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 Computer Vision - Image Analysis

  Why it's wrong here

  Azure Computer Vision - Image Analysis is a general-purpose visual understanding service. It can detect faces in an image and return attributes like age, emotion, or whether a person is wearing glasses, but it does not create an identity signature or compare two separate face images to determine whether they belong to the same person. Therefore, it lacks the core 1:1 face verification capability required here.

  When this WOULD be correct

  A company wants to automatically generate captions for product images and detect common objects like 'car' or 'tree' in photos. In that scenario, Azure Computer Vision - Image Analysis would be the correct service because it provides image description and object detection features.

• ✓

  Azure Face API

  Why this is correct

  Azure Face API is the correct choice because it provides a dedicated face verification endpoint that performs 1:1 matching. The service detects facial landmarks in a live photo, extracts a unique face embedding, and compares it against a reference photo (such as an ID document) to compute a similarity score against a configurable confidence threshold. This is precisely the identity-verification scenario the retail company needs.

• ✗

  Azure Custom Vision

  Why it's wrong here

  Azure Custom Vision is a model training service for building custom image classifiers or object detectors from your own labeled datasets. While you could theoretically train a model to recognize a specific person's face, it does not offer a prebuilt, turnkey face verification endpoint with facial landmark detection, similarity scoring, and confidence thresholds. It also requires extensive training data and management, making it unsuitable for out-of-the-box identity verification against an ID photo.

  When this WOULD be correct

  A company needs to build a custom model to identify specific animal species in wildlife camera images, where no pre-built service exists. Azure Custom Vision would be correct because it allows training a tailored image classifier with their own labeled dataset.

• ✗

  Azure Form Recognizer

  Why it's wrong here

  Azure Form Recognizer is a document intelligence service that extracts text, tables, and key-value pairs from forms and documents using OCR and layout analysis. It processes document structure and printed or handwritten content but does not analyze facial features or compare images of people. Hence, it is completely unrelated to the face verification requirement.

  When this WOULD be correct

  A company needs to automatically extract data (e.g., name, ID number, expiration date) from scanned government-issued IDs and then validate that data against a database. In that case, Form Recognizer would be the correct service for document data 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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