AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail store wants to analyze customer movement patterns, such as dwell time in front of displays and foot traffic heatmaps, using existing surveillance cameras. Which Azure Computer Vision capability is most suitable?
⚠ Common exam trap
It's easy for candidates to confuse object detection (which finds objects in a single frame) with spatial analysis (which tracks movement over time across multiple frames), leading them to pick object detection for a scenario that requires temporal and spatial tracking.
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 choice because it is specifically designed to analyze people's presence, movement, and interactions within a physical space using video feeds. It can measure dwell time in front of displays and generate foot traffic heatmaps by tracking individuals across camera frames, which directly matches the retail store's requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Object detection
Why it's wrong here
Object detection is a computer vision technique that identifies and localizes objects within a single image or frame, typically drawing bounding boxes around entities like people. While it can detect customers in each video frame, it lacks any temporal or tracking logic to associate the same person across frames, nor can it compute dwell time, path trajectories, or aggregate foot-traffic heatmaps. For retail movement analysis, object detection would only provide per-frame presence, not the continuous behavioral insights required.
- ✗
Optical character recognition (OCR)
Why it's wrong here
Optical character recognition (OCR) is designed to extract machine-readable text from images or documents, such as reading price tags, signage, or receipts. It has no mechanism to detect humans, locate their positions in space, or track their movement through a store. In a retail video analytics context, OCR might read a promotional banner but can contribute nothing to understanding customer movement patterns like dwell time or route flow.
- ✓
Spatial analysis
Why this is correct
Spatial analysis, as offered in Azure Video Indexer and similar services, is specifically built for understanding people's positions and movements within a video scene. It uses person detection combined with tracking algorithms to follow individuals across frames, measure how long they remain in an area (dwell time), and aggregate data into heatmaps and foot-traffic patterns. This makes spatial analysis the correct service for a retail store wanting to analyze customer movement patterns.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single category label to an entire image, such as deciding whether a photo shows a 'store interior' or 'warehouse'. It treats the whole image as one unit and does not identify individual people, their locations, or their interactions with the environment. Since it processes each image independently with no time-sequence awareness, it cannot track movement or compute metrics like dwell time or heatmaps across a video stream.
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