AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is the difference between Azure AI Vision and Azure AI Custom Vision?
⚠ Common exam trap
Many candidates confuse 'pre-built' with 'faster' or 'more accurate,' or assume both services are interchangeable, when in fact the key differentiator is whether you need to provide your own labeled training data (Custom Vision) or can rely on Microsoft's pre-trained models (Azure AI Vision).
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
✓
Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images
Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Vision is faster; Custom Vision is more accurate
Why it's wrong here
This is not a supported performance claim. Azure AI Vision's pre-built models are specialized for common scenarios, but a Custom Vision model trained on a narrow domain can outperform them on that domain. At the same time, Custom Vision is not guaranteed to be more accurate than the general models—accuracy depends on training data, model type, and validation. The real difference is whether you need a ready-made generic model or a domain-specific trained model.
- ✓
Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images
Why this is correct
Azure AI Vision provides ready-to-use, pre-built models through REST APIs and SDKs for common computer vision tasks, so no training or labeled data is required. Custom Vision lets you define your own classes by uploading labeled images and training a custom image classification or object detection model. This is the core distinction in the exam: use Azure AI Vision for general capabilities, and Azure Custom Vision when your images need a specialized model trained on your own dataset.
- ✗
Azure AI Vision analyzes only photos; Custom Vision analyzes documents
Why it's wrong here
Azure AI Vision and Custom Vision both operate on images, and both can process photos. Azure AI Vision includes document-related optical character recognition (OCR), while Custom Vision is designed for image classification and object detection—not document text extraction. Therefore the input type is not the differentiator; the differentiator is pre-trained general-purpose versus custom-trained models.
- ✗
They are different names for the same service
Why it's wrong here
They are distinct Azure services, not alias names. Azure AI Vision ships pre-trained, general-purpose vision models for tasks like OCR, image description, and object detection out of the box. Custom Vision is a separate resource that requires the user to upload and label their own images, then trains a bespoke classification or object detection model. Calling them the same service hides this major workflow and training difference.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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