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 scans thousands of packages daily. They need an automated system to read handwritten shipping labels to sort packages correctly. Which Azure Computer Vision capability should they use?
Why wrong: Image Analysis can generate captions and tags for images, but it does not extract the specific text characters needed for reading shipping labels.
B
Optical Character Recognition (OCR)
OCR in Azure AI Vision is designed to extract printed and handwritten text from images, making it ideal for reading shipping labels.
C
Object Detection
Why wrong: Object Detection identifies and locates objects (e.g., a box), but it does not read text on labels.
D
Face API
Why wrong: Face API detects and analyzes human faces; it is not used for text extraction.
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)
The correct answer is B, Optical Character Recognition (OCR), because the scenario requires extracting handwritten text from images of shipping labels to automate sorting. OCR is the specific Azure Computer Vision capability designed to detect and read printed or handwritten text from images, returning machine-readable text that can be used for downstream processing.
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 can generate captions and tags for images, but it does not extract the specific text characters needed for reading shipping labels.
✓
Optical Character Recognition (OCR)
Why this is correct
OCR in Azure AI Vision is designed to extract printed and handwritten text from images, making it ideal for reading shipping labels.
Related concept
Read the scenario before looking for a memorised answer.
✗
Object Detection
Why it's wrong here
Object Detection identifies and locates objects (e.g., a box), but it does not read text on labels.
✗
Face API
Why it's wrong here
Face API detects and analyzes human faces; it is not used for text extraction.
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 scenes) with OCR, but Image Analysis does not extract text—it only provides visual descriptions and tags.
Detailed technical explanation
How to think about this question
Azure Computer Vision's OCR API uses deep-learning models trained on both printed and handwritten text, supporting multiple languages and orientations. Under the hood, it performs text detection (finding text regions) followed by text recognition (converting image regions to characters), returning results with bounding boxes and confidence scores. In a real-world logistics scenario, OCR can be integrated with Azure Logic Apps or Functions to trigger sorting decisions based on extracted address data.
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) — The correct answer is B, Optical Character Recognition (OCR), because the scenario requires extracting handwritten text from images of shipping labels to automate sorting. OCR is the specific Azure Computer Vision capability designed to detect and read printed or handwritten text from images, returning machine-readable text that can be used for downstream processing.
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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