AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail store uses security cameras to analyze customer behavior. They need to detect when a person enters a specific zone (e.g., an aisle) and count how many people are in that zone at any given time. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse Object Detection with Spatial Analysis because both can detect people in a frame, but Object Detection lacks the temporal and spatial reasoning (zone/line crossing, tracking, counting) required for this scenario.
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
✓
Spatial Analysis
Spatial Analysis is the correct Azure Computer Vision capability because it is specifically designed to analyze video feeds from cameras to detect people entering predefined zones, track their movement, and count occupancy in real time. This capability uses AI models to understand spatial relationships and events within a video frame, such as a person crossing a line or entering a zone, which directly matches the requirement to detect when a person enters a specific aisle and count how many people are in that zone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Spatial Analysis
Why this is correct
Spatial Analysis is the correct choice because it is an Azure AI Vision service purpose-built for real-time video understanding of people in physical spaces. It detects and tracks individuals across frames and applies camera calibration to map pixel locations to real-world coordinates, enabling zone-based operations such as entry detection, exit detection, and head counts. This temporal tracking and geospatial reasoning is exactly what a retail store needs to analyze customers as they pass through camera-monitored zones.
- ✗
Object Detection
Why it's wrong here
Object Detection is incorrect because it localizes instances of people in a single image frame by drawing bounding boxes with confidence scores, but it does not maintain identity or position across frames. Without temporal tracking, it cannot determine whether a person has moved into a defined zone, crossed a boundary, or is a new entrant versus someone already counted. A frame-level detection may produce a momentary head count, but it cannot reliably produce the entry and exit counts required by the store's zone-based analysis.
- ✗
Image Classification
Why it's wrong here
Image Classification is incorrect because it assigns one or more global labels to an entire image, such as 'retail store' or 'shopping aisle', with no object localization or counting capability. It treats the whole scene as a single category and cannot distinguish individual people, let alone measure how many customers enter or exit a particular zone. Therefore, even though a model might classify a camera view as 'customers present', it provides none of the per-person spatial data the retail analysis requires.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is incorrect because it detects and extracts text characters and words from images, such as signs, receipts, or product labels, using the Read API. It has no person-detection component and no concept of spatial zones, people movement, or head counts. The store's analysis task is about people, not text, so OCR would contribute nothing to counting customers or tracking their entry and exit patterns.
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