AI-900 Practice Question: Describe features of computer vision workloads on Azure
A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
The trap here is that candidates might confuse face detection with people counting, but face detection fails when faces are not visible, whereas object detection with the 'person' class is more robust for counting people in a crowd.
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
✓
C. Object detection
Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A. Optical Character Recognition (OCR)
Why it's wrong here
OCR is a computer vision technique that extracts machine-readable text from images or video frames, such as reading street signs or license plates. It operates on character shapes and language patterns, not on physical objects or people. Since the transportation department needs to count people in a live camera feed, OCR cannot provide any person detection or counting capability.
- ✗
B. Face detection
Why it's wrong here
Face detection is a specialized form of object detection that locates human faces by identifying facial features like eyes, noses, and mouths, typically drawing a bounding box around the face. It fails to count people whose faces are turned away, partially obscured, or too small to resolve in a crowded scene. Even when faces are visible, it counts faces, not whole people, so a person with their back to the camera would be completely missed, making it unreliable for accurate pedestrian counts.
- ✓
C. Object detection
Why this is correct
Object detection is the correct approach because it is designed to locate and classify each occurrence of a predefined object class—such as 'person'—within an image or video frame, outputting a bounding box and a confidence score for each instance. Modern detectors like YOLO or Faster R-CNN use features from the entire body, not just the face, so they can detect people regardless of their orientation or whether their face is visible. In a live camera feed, object detection models run at real-time speeds and can count every detected person by tallying the number of bounding boxes labeled 'person', making it ideal for the transportation department's need.
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D. Semantic segmentation
Why it's wrong here
Semantic segmentation assigns a class label to every single pixel in an image, partitioning the frame into regions such as 'person', 'road', or 'building'. However, it does not distinguish between separate instances of the same class—all adjacent people merge into one large pixel region labeled 'person'. Therefore, it cannot directly provide a count of individual people; to count distinct people, instance segmentation would be required, which is a different and more complex task than what semantic segmentation offers.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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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