AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A logistics company needs to automatically read shipping labels on packages. The labels contain printed text in various fonts and sizes, as well as handwritten addresses. Which Azure Computer Vision capability should they use to extract the text from the labels?
Why wrong: Image Analysis extracts tags, captions, and descriptions of image content, but it does not extract text from images.
B
Face API
Why wrong: Face API is used for detecting, recognizing, and analyzing human faces, not for text extraction.
C
Optical Character Recognition (OCR) - Read API
The Read API is purpose-built for extracting printed and handwritten text from images and documents, supporting various fonts and sizes.
D
Custom Vision
Why wrong: Custom Vision allows you to train custom image classification or object detection models, but it is not designed for general-purpose text extraction from labels.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Optical Character Recognition (OCR) - Read API
The Read API (part of Azure Computer Vision's OCR capabilities) is specifically designed to extract printed and handwritten text from images, handling varied fonts, sizes, and styles. This makes it the correct choice for reading shipping labels that contain both printed text and handwritten addresses.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Analysis
Why it's wrong here
Image Analysis extracts tags, captions, and descriptions of image content, but it does not extract text from images.
✗
Face API
Why it's wrong here
Face API is used for detecting, recognizing, and analyzing human faces, not for text extraction.
✓
Optical Character Recognition (OCR) - Read API
Why this is correct
The Read API is purpose-built for extracting printed and handwritten text from images and documents, supporting various fonts and sizes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Custom Vision
Why it's wrong here
Custom Vision allows you to train custom image classification or object detection models, but it is not designed for general-purpose text extraction from labels.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Image Analysis (which can detect text in images but not extract it reliably from mixed formats) with the dedicated OCR Read API, or they mistakenly think Custom Vision can be trained for text extraction when it is designed for custom visual patterns.
Detailed technical explanation
How to think about this question
The Read API uses a two-step process: first, it returns an operation ID for asynchronous processing, then it retrieves the results containing bounding boxes, lines, and words with confidence scores. It supports over 200 languages and can handle both printed and handwritten text in a single image, making it ideal for logistics scenarios where label formats vary. The API also returns the text in the order it appears on the label, preserving reading order for downstream processing.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Optical Character Recognition (OCR) - Read API — The Read API (part of Azure Computer Vision's OCR capabilities) is specifically designed to extract printed and handwritten text from images, handling varied fonts, sizes, and styles. This makes it the correct choice for reading shipping labels that contain both printed text and handwritten addresses.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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