AI-900 Practice Question: Describe features of computer vision workloads on Azure
A security company needs to monitor a warehouse using video cameras. They want to detect whether any persons are present in a given frame and also know their approximate locations. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Candidates often confuse object detection with image classification, thinking that simply labeling an image as containing a person is sufficient, but the question explicitly requires 'approximate locations' which only object detection provides.
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 choice because it not only identifies whether persons are present in a video frame but also provides bounding box coordinates indicating their approximate locations. This capability is specifically designed to locate multiple objects of interest within an image, which directly matches the requirement of detecting persons and knowing where they are.
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 analyzes an entire image and assigns it a single class label, such as 'person present' or 'no person.' It cannot output bounding boxes, coordinates, or any spatial information about where each person is located. Even with techniques like sliding windows, image classification alone is inefficient and provides only coarse, unreliable localization. Therefore, it fails the requirement to know the location of each person in the warehouse.
- ✓
Object detection
Why this is correct
Object detection is specifically designed to locate and classify multiple objects within an image. Algorithms like YOLO, Faster R-CNN, or SSD output both class labels (e.g., 'person') and bounding box coordinates for each detected instance. This directly satisfies the security company's need to know that persons are present and where they are positioned in the warehouse. The bounding boxes provide approximate locations, which perfectly matches the stated requirement.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel in an image into a category (e.g., person, floor, wall), producing a dense pixel-level map. This fails the scenario because the requirement is only to detect whether persons are present and know their approximate locations, not to label every pixel in the frame. It is tempting because segmentation can locate objects precisely, but that precision is unnecessary overhead here; object detection would be the correct choice for bounding-box localisation without pixel-level detail.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is a computer vision technique that extracts printed or handwritten text from images, such as read license plates, labels, or documents. It does not detect people or any other physical objects, nor does it provide spatial locations of individuals. Because the warehouse monitoring scenario requires person detection and localization, OCR is completely irrelevant and cannot address the need.
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Azure Machine Learning Studio
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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.
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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.