AI-900 Practice Question: Describe features of computer vision workloads on Azure
A security company wants to use Azure Computer Vision to monitor a restricted area. They need to count the number of people present in each camera frame and draw bounding boxes around each person. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Many candidates confuse face detection (which only finds faces) with object detection (which finds full people), leading candidates to choose Face detection when the requirement is to count people regardless of face visibility.
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
✓
Image Analysis (object detection)
(Image Analysis with object detection) is correct because Azure Computer Vision's object detection capability can identify and locate multiple instances of a specific object class—in this case, people—within an image. It returns bounding box coordinates for each detected person, enabling the security company to count individuals and draw boxes around them in each camera frame.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR), implemented in Azure AI Vision's Read API, extracts printed or handwritten text from images and returns it as machine-readable characters with word-level bounding boxes. It has no model for detecting people as physical objects, so it cannot count individuals or provide their locations. Thus, OCR would be inappropriate for monitoring people even if the image contains text that happens to be visible.
- ✓
Image Analysis (object detection)
Why this is correct
Image Analysis with object detection is correct because Azure AI Vision's object detection feature identifies instances of trained object categories, including person, and returns a bounding box and confidence score for each detection. By counting the returned person instances, the system can report how many people are present and where they are located in the frame. This directly supports security monitoring scenarios that need to detect and track people in an image or video frame.
- ✗
Face detection
Why it's wrong here
Face detection locates human faces and returns facial rectangles plus attributes such as head pose or blur, but it does not detect entire body shapes. If a person's face is turned away, occluded, too small, or outside the frame while their body is visible, face detection will not count them; conversely, one person with multiple face-like angles does not equate to multiple people. For security monitoring that requires counting all people, face detection is therefore an incomplete and unreliable approach.
- ✗
Image classification
Why it's wrong here
Image classification assigns a label or set of labels to the whole image—for example, it might label a scene 'crowd' or 'people'—but it provides no information about where each person is or how many distinct individuals are present. Because classification produces one or more tag-level predictions for the entire picture rather than instance-level detections, it cannot generate a per-person count or bounding boxes. Used alone, it cannot satisfy a monitoring requirement that depends on enumerating people.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.