AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse semantic segmentation with object detection because both involve 'segments' or 'regions,' but segmentation does not separate individual instances of the same object type, making it unsuitable for counting distinct boxes.
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 identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.
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 this is correct
Object detection is a computer vision technique that identifies instances of defined classes within an image and localizes each instance with a bounding box. For a warehouse camera that must 'locate and frame' each box, object detection outputs the coordinates and dimensions of every detected box, enabling downstream tasks like robotic picking or inventory tracking. Unlike classification, it provides spatial localization rather than a single image-level label.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single category label to the entire image, such as 'warehouse shelf' or 'containers', without any spatial localization of individual objects. It treats the whole image as one unit and cannot answer 'where' a box is. Therefore it fails the core requirement of locating and framing each box, which demands object-level spatial output.
When this WOULD be correct
A question asking: 'Which Azure Computer Vision capability should be used to determine whether a shelf contains boxes or is empty?' would make image classification correct, as it outputs a single category for the whole image.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel in the image into a semantic category, producing a dense pixel-level mask where each box's exact silhouette is colored or labeled. While this gives far more precise boundaries than a rectangle, it does not naturally return a bounding box, which is what the requirement asks for. In a warehouse setting, it would be overkill and require additional post-processing to derive rectangular frames from the segmentation mask.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) extracts machine-encoded text from images, typically using detection and recognition models to find text regions and transcribe characters. It is entirely unsuited to locating physical objects such as boxes because it operates on text glyphs and their positions, not on object instances. Even if a box has a label with text, OCR would locate the text, not the box's extent.
When this WOULD be correct
A question asking to extract printed or handwritten text from images, such as reading serial numbers on boxes or license plates, where the output is the text content rather than object locations.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Object detectionCorrect answer▾
Why this is correct
Object detection is a computer vision technique that identifies instances of defined classes within an image and localizes each instance with a bounding box. For a warehouse camera that must 'locate and frame' each box, object detection outputs the coordinates and dimensions of every detected box, enabling downstream tasks like robotic picking or inventory tracking. Unlike classification, it provides spatial localization rather than a single image-level label.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, but the question requires locating and drawing bounding boxes around individual boxes, which is object detection's function.
★ When this WOULD be the correct answer
A question asking: 'Which Azure Computer Vision capability should be used to determine whether a shelf contains boxes or is empty?' would make image classification correct, as it outputs a single category for the whole image.
Why candidates choose this
Candidates may confuse image classification with object detection, thinking that identifying objects in an image is the same as classifying the image content, without realizing the need for spatial localization.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR extracts text from images, but the question asks for locating and drawing rectangular frames around boxes, which requires detecting objects (boxes) and their positions, not reading text.
★ When this WOULD be the correct answer
A question asking to extract printed or handwritten text from images, such as reading serial numbers on boxes or license plates, where the output is the text content rather than object locations.
Why candidates choose this
Candidates may confuse 'locating boxes' with 'locating text' or think OCR can detect any rectangular region, not realizing OCR is specifically for text recognition.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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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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.