AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail company wants to build a solution that automatically reads the printed text on product labels to update inventory records. The labels contain alphanumeric characters and are in various fonts and sizes. Which Azure Cognitive Service should they use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Form Recognizer (which includes OCR capabilities) with the simpler Computer Vision OCR service, but Form Recognizer is designed for structured document extraction, not general-purpose text reading from labels.
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 Computer Vision - OCR
Azure Computer Vision's OCR (Optical Character Recognition) API is specifically designed to extract printed text from images, handling various fonts, sizes, and alphanumeric characters. This makes it the ideal choice for reading product labels to update inventory records, as it can process the diverse label formats commonly found in retail environments.
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 Face Service
Why it's wrong here
Azure Face Service is a specialized cognitive service for detecting, analyzing, and recognizing human faces in images, including attributes like age, emotion, and facial landmarks. It does not perform optical character recognition or extract any alphanumeric content from product labels. Choosing it for label text extraction would fail because the service ignores text and instead focuses on face geometry and identity-related tasks.
- ✗
Azure Form Recognizer
Why it's wrong here
Azure Form Recognizer is incorrect because it specialises in extracting structured data from documents like forms, invoices, or receipts, where fields are typically in consistent locations. Product labels, especially with varying fonts and sizes, often lack this predictable structure, requiring general text extraction rather than field-specific data parsing. It is tempting as it extracts text from images, but its strength lies in understanding document layouts to identify and extract specific key-value pairs or table data from semi-structured or structured documents.
- ✓
Azure Computer Vision - OCR
Why this is correct
Azure Computer Vision's OCR (now delivered through the Read API) is the correct choice because it applies optical character recognition to extract printed and handwritten text from images, including varied fonts and label layouts. It returns structured output with words, lines, and bounding boxes, making it suitable for the unstructured text on product labels. Unlike document-intelligence services, it does not require predefined fields, so it handles arbitrary label designs well. This directly meets the retail company's need to automatically read text from product label images.
- ✗
Azure Video Indexer
Why it's wrong here
Azure Video Indexer is built for analyzing media files to produce insights such as shot boundaries, speech-to-text, faces, and optical character recognition only as part of video frame analysis. It is not designed for processing a single static image of a product label, and invoking it would require uploading a video artifact and managing a video indexing pipeline. The scenario is a simple image-text extraction task, so this service adds unnecessary complexity and is the wrong fit.
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Azure Machine Learning Studio
Key term
Inventory
Inventory is the complete list of hardware, software, and network assets that an organization owns or manages, tracked for maintenance, security, and budgeting purposes.
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.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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