The answer is Optical Character Recognition (OCR). This is the correct choice because Azure Computer Vision’s OCR capability, powered by the Read API, is specifically built to extract both printed and handwritten text from images, including complex forms. Unlike other vision features that analyze objects or scenes, the Read API is optimized for text-heavy documents and can handle varied handwriting styles, making it ideal for automating data entry from handwritten forms. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of which prebuilt service maps to a specific text-extraction scenario—a common trap is confusing OCR with Form Recognizer, but remember that OCR focuses purely on raw text extraction, while Form Recognizer adds structure like key-value pairs. For a quick memory tip, think “OCR = Optical Character Reading” for any image-to-text task, whether printed or handwritten.
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 company wants to use Azure Computer Vision to automatically analyze images of handwritten forms and extract the text for data entry. Which prebuilt Azure Computer Vision capability should they use?
OCR, specifically the Read API in Azure Computer Vision, is designed to extract text from images, including handwritten text, and converts it into machine-readable text.
B
Image Analysis
Why wrong: Image Analysis can describe visual content, tag objects, and generate captions, but it does not extract text from images.
C
Face API
Why wrong: The Face API detects, identifies, and analyzes human faces in images. It is not used for text extraction.
D
Object Detection
Why wrong: Object Detection identifies and locates objects in images by drawing bounding boxes. It does not read or extract text.
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)
Azure Computer Vision's Optical Character Recognition (OCR) capability is specifically designed to extract printed or handwritten text from images, including forms. It uses the Read API, which is optimized for text-heavy documents and supports handwritten text recognition, making it the correct choice for this scenario.
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.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR, specifically the Read API in Azure Computer Vision, is designed to extract text from images, including handwritten text, and converts it into machine-readable text.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image Analysis
Why it's wrong here
Image Analysis can describe visual content, tag objects, and generate captions, but it does not extract text from images.
✗
Face API
Why it's wrong here
The Face API detects, identifies, and analyzes human faces in images. It is not used for text extraction.
✗
Object Detection
Why it's wrong here
Object Detection identifies and locates objects in images by drawing bounding boxes. It does not read or extract text.
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 as a general feature) with the dedicated OCR capability, but Image Analysis does not provide the same level of handwritten text extraction accuracy or structured output as the Read API.
Detailed technical explanation
How to think about this question
The OCR capability in Azure Computer Vision leverages the Read API, which uses a deep-learning model trained on both printed and handwritten text. It returns bounding boxes and text content in a hierarchical structure (pages, lines, words), and supports multiple languages. In real-world scenarios, this is used for automated data entry from forms like medical intake sheets or surveys, where accuracy depends on the model's ability to handle varied handwriting styles.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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) — Azure Computer Vision's Optical Character Recognition (OCR) capability is specifically designed to extract printed or handwritten text from images, including forms. It uses the Read API, which is optimized for text-heavy documents and supports handwritten text recognition, making it the correct choice for this scenario.
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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