AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail chain wants to analyze in-store security camera feeds to count the number of customers entering the store each hour. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
A common mix-up: candidates confuse object detection with image classification, thinking that classifying an image as 'crowded' or 'empty' is sufficient for counting, when in fact object detection is required to enumerate individual instances.
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
✓
Object detection
Object detection is the correct capability because it can identify and locate multiple instances of 'person' objects within each video frame, then track and count them over time to determine the number of customers entering per hour. Image classification only labels the entire image with a single category, which cannot provide per-object counts or spatial locations needed for accurate customer 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.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single label (e.g., 'crowded', 'indoor') to the entire image via a CNN's softmax output, but it produces no bounding boxes or per-object counts. For a security camera feed, it could tell you the scene appears busy, but it cannot tell you how many individual people are present in the frame. This lack of localization makes it unsuitable for measuring foot traffic.
- ✓
Object detection
Why this is correct
Object detection solves this exact problem by using models like Faster R-CNN, YOLO, or SSD to output bounding boxes and class labels for every person in each frame. By counting the number of 'person' detections per frame, the system can estimate foot traffic, and with tracking across frames (e.g., IoU or re-ID), it can avoid double-counting the same person. This is the correct Azure AI service capability for in-store people counting.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) extracts printed or handwritten text from images using the Azure AI Vision Read API, converting regions of interest into machine-readable strings. It has no understanding of objects like humans; even if a person's t-shirt had text, OCR would only capture the characters, not the person or their count. Thus it is entirely irrelevant to analyzing people in security camera footage.
- ✗
Facial recognition
Why it's wrong here
Facial recognition goes beyond detection by identifying or verifying an individual's identity using facial embeddings, typically requiring a pre-enrolled database of known faces. For counting foot traffic, it is overkill and fails because it only works for recognized faces—not anonymous shoppers—and it raises significant privacy, consent, and regulatory concerns (e.g., GDPR). Azure's Face API is designed for identification/verification, not general population counting.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
Key term
Image classification
Image classification is the process of teaching a computer to look at a picture and decide what category or label best describes the main object or scene in that picture.
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