AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail company uses overhead cameras to monitor shelf inventory in a store. They want to build a system that automatically detects whether a shelf section is empty or stocked, and specifically identify product categories (e.g., 'soft drinks', 'chips', 'canned goods') and count the number of items in each category. The company has a large set of labeled images showing different shelf states. Which Azure Computer Vision service should they use to build this custom detection and counting solution?
⚠ Common exam trap
It's easy for candidates to confuse pre-built Computer Vision features (like dense captioning or OCR) with Custom Vision, assuming any Azure Computer Vision service can be customized without training, but only Custom Vision supports custom object detection with bounding boxes and counting.
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
✓
Custom Vision object detection
Custom Vision object detection is the correct choice because it allows the company to train a model on their labeled images to detect and localize specific product categories (e.g., 'soft drinks', 'chips') and count items within each category. Unlike pre-built Computer Vision features, Custom Vision enables custom object detection with bounding boxes and classification, which directly supports the requirement for detecting shelf states and counting items per category.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Computer Vision Image Analysis with dense captioning
Why it's wrong here
Dense captioning generates descriptive captions for regions of an image, but it does not provide the precise object detection and counting capabilities needed for inventory management. It is not designed for training on custom categories.
When this WOULD be correct
A company wants to automatically generate natural language descriptions of scenes in images, such as 'a shelf with soft drinks and chips', without needing to count or classify specific object categories. They have labeled images with region descriptions.
- ✓
Custom Vision object detection
Why this is correct
Custom Vision object detection is specifically designed for training models to detect and locate objects of interest. With labeled images of product categories, you can create a model that outputs bounding boxes around each detected item, enabling counting.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is used to extract printed or handwritten text from images. Shelf items may have text, but the core requirement is to detect and count product categories, not to read text. OCR alone cannot identify product categories visually.
- ✗
Azure Machine Learning with a pre-trained YOLO model
Why it's wrong here
While technically possible to use a pre-trained model via Azure Machine Learning, the question asks for an Azure Computer Vision service. Custom Vision is a dedicated PaaS offering that abstracts the complexity and is the most straightforward Azure service for this task.
When this WOULD be correct
A question where a company needs to build a custom object detection model but has specific requirements for using a pre-trained model (e.g., YOLO) due to edge deployment constraints, and they have the expertise to manage the ML lifecycle on Azure Machine Learning.
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.
✓Custom Vision object detectionCorrect answer▾
Why this is correct
Custom Vision object detection is specifically designed for training models to detect and locate objects of interest. With labeled images of product categories, you can create a model that outputs bounding boxes around each detected item, enabling counting.
✗Computer Vision Image Analysis with dense captioningWrong answer — click to see why▾
Why this is wrong here
Dense captioning generates descriptive captions for regions of an image, but it does not provide structured object detection with bounding boxes and counts per category, which is required for counting items per product category.
★ When this WOULD be the correct answer
A company wants to automatically generate natural language descriptions of scenes in images, such as 'a shelf with soft drinks and chips', without needing to count or classify specific object categories. They have labeled images with region descriptions.
Why candidates choose this
Candidates may think dense captioning can identify and describe objects in shelves, but they overlook that it lacks the precise localization and counting capabilities needed for inventory management.
✗Azure Machine Learning with a pre-trained YOLO modelWrong answer — click to see why▾
Why this is wrong here
Azure Machine Learning with a pre-trained YOLO model is not a managed Azure Computer Vision service; it requires custom model training and deployment, whereas Custom Vision provides a simpler, integrated solution for custom object detection without managing infrastructure.
★ When this WOULD be the correct answer
A question where a company needs to build a custom object detection model but has specific requirements for using a pre-trained model (e.g., YOLO) due to edge deployment constraints, and they have the expertise to manage the ML lifecycle on Azure Machine Learning.
Why candidates choose this
Candidates may know YOLO is a popular object detection model and think Azure Machine Learning is the only way to use it, overlooking that Custom Vision internally uses similar deep learning models but offers a more streamlined service for this use case.
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
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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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.