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 library wants to digitize a collection of old printed books by converting scanned pages into searchable, editable text. Which Azure Computer Vision capability should they use?
Why wrong: Image Analysis generates captions and tags for images but does not extract the actual text content in a structured, searchable form.
B
Optical Character Recognition (OCR)
OCR is designed specifically to detect and extract text from images, making it the ideal choice for converting scanned book pages into editable and searchable text.
C
Object detection
Why wrong: Object detection identifies and locates objects within an image, but it is not designed to extract text.
D
Face detection
Why wrong: Face detection finds human faces in images; it cannot 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)
Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For digitizing old printed books, OCR can process scanned pages to produce digital text that can be indexed and edited, directly meeting the library's requirement.
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 (descriptions and tags)
Why it's wrong here
Image Analysis generates captions and tags for images but does not extract the actual text content in a structured, searchable form.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR is designed specifically to detect and extract text from images, making it the ideal choice for converting scanned book pages into editable and searchable text.
Related concept
Read the scenario before looking for a memorised answer.
✗
Object detection
Why it's wrong here
Object detection identifies and locates objects within an image, but it is not designed to extract text.
✗
Face detection
Why it's wrong here
Face detection finds human faces in images; it cannot extract text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Image Analysis (which can describe a scene containing text) with OCR (which specifically extracts the text itself), leading them to choose option A when the task requires editable text output.
Detailed technical explanation
How to think about this question
Azure Computer Vision's OCR uses deep-learning models trained on millions of text images to recognize characters at the word and line level, supporting printed text in multiple languages. Under the hood, the OCR API returns bounding boxes, text content, and confidence scores for each detected word, enabling downstream processing like search indexing or text-to-speech. In real-world scenarios, OCR can handle skewed or degraded text in old books, but accuracy may drop with heavy page damage or unusual fonts, requiring preprocessing like binarization or deskewing.
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) — Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For digitizing old printed books, OCR can process scanned pages to produce digital text that can be indexed and edited, directly meeting the library's requirement.
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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