AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'face detection' vs 'face identification' in Azure AI Vision?
⚠ Common exam trap
It's easy for candidates to confuse the terms 'detection' and 'identification' as interchangeable, when Azure explicitly separates them as two distinct API operations with different capabilities and pricing tiers.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Detection locates faces and returns attributes; identification matches faces to a known person database
Face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Face detection and identification are the same feature with different names
Why it's wrong here
Detection and identification are distinct Azure Face service operations invoked with different API endpoints and parameters: Detect returns faceId, face rectangles, and attributes, while Identify requires that faceId as input and searches a PersonGroup to find the closest enrolled person. They are not interchangeable aliases — detection is a prerequisite for identification, and identification cannot be performed without first producing a faceId via detection. Because they serve different purposes and follow a sequential dependency, calling them the same feature with different names misrepresents the architecture and the responsible AI safeguards that apply specifically to identification.
- ✓
Detection locates faces and returns attributes; identification matches faces to a known person database
Why this is correct
The Azure Face API Detect operation first localizes one or more faces in an image by returning bounding-box coordinates, then optionally extracts attributes such as age, emotion, or head pose. In contrast, the Identify operation takes a detected face's unique faceId and compares it against a PersonGroup or LargePersonGroup that has been pre-enrolled with known individuals, returning the best-matching person or an empty result. This 'detect-then-identify' pipeline is why the core distinction is localization/attribute extraction versus association with a known identity, and why identification requires a registered database plus the responsible AI gating that Microsoft applies to facial recognition scenarios.
- ✗
Detection works on live video; identification works only on still images
Why it's wrong here
This option confuses media type with capability: Azure Face detection and identification both operate on still images, while video analysis is achieved by extracting frames and sending each frame to the same Face API operations. Detection works on live video streams only if you build a frame-by-frame pipeline, and identification can equally work on those extracted video frames. The real differentiator is not whether the input is video or a photo, but what the API returns — a bounding box plus attributes versus a matched identity from a known-person database.
- ✗
Face detection requires a paid tier; identification is available in the free tier
Why it's wrong here
Azure Face API pricing is transaction-based and does not separate detection from identification into paid versus free feature buckets; both operations incur per-call charges and are subject to the same limited-access policies. A free-tier subscription can call both Detect and Identify, and a paid tier does not unlock identification as an exclusive capability. The functional distinction remains that detection localizes faces and returns attributes, while identification requires a pre-enrolled person database and returns a name or 'no match' — cost is a billing consideration, not the conceptual difference.
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Azure Machine Learning Studio
Key term
Azure AI Vision
Azure AI Vision is a cloud-based service from Microsoft that uses pre-built machine learning models to extract information from images and videos, such as objects, text, faces, and scene descriptions.
Key term
Face detection
Face detection is an AI service that identifies and locates human faces in images or video, distinguishing them from other objects or backgrounds.
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