- A
Optical Character Recognition (OCR)
Correct because OCR is the technology used to extract text from images and documents, including handwritten text. Azure AI Computer Vision includes OCR capabilities.
- B
Image Analysis
Why wrong: Incorrect because Image Analysis provides descriptive information about an image's content (objects, scenes, tags) but does not extract text strings.
- C
Face detection
Why wrong: Incorrect because face detection identifies human faces in an image, not text content.
- D
Custom Vision
Why wrong: Incorrect because Custom Vision is used to train custom image classification or object detection models. While it could theoretically detect text regions, it is not optimized for text extraction and requires extensive training data, whereas OCR is a prebuilt, ready-to-use feature.
Quick Answer
The answer is Optical Character Recognition (OCR), specifically Azure’s OCR API within Computer Vision, because it is the only service designed to extract printed and handwritten text from images, such as the destination address, sender name, and package weight on shipping forms. OCR works by detecting text regions, recognizing characters through deep learning models trained on both printed and cursive handwriting, and returning the extracted text in a structured format. On the AI-900 exam, this scenario tests your understanding of which Computer Vision capability handles text extraction versus image analysis or object detection—a common trap is confusing OCR with Form Recognizer, but OCR is the correct choice for raw text extraction from handwritten documents. Remember the memory tip: “OCR reads the words, Form Recognizer reads the forms.”
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 receives thousands of handwritten shipping forms daily. They need an automated solution to extract the destination address, sender name, and package weight from these forms. Which Azure Computer Vision capability should they use?
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
Optical Character Recognition (OCR)
The correct answer is A, Optical Character Recognition (OCR), because the task requires extracting text (destination address, sender name, package weight) from handwritten shipping forms. Azure's OCR API, part of Computer Vision, is specifically designed to detect and read printed and handwritten text from images, making it the appropriate capability for this document processing 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
Correct because OCR is the technology used to extract text from images and documents, including handwritten text. Azure AI Computer Vision includes OCR capabilities.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Image Analysis
Why it's wrong here
Incorrect because Image Analysis provides descriptive information about an image's content (objects, scenes, tags) but does not extract text strings.
- ✗
Face detection
Why it's wrong here
Incorrect because face detection identifies human faces in an image, not text content.
- ✗
Custom Vision
Why it's wrong here
Incorrect because Custom Vision is used to train custom image classification or object detection models. While it could theoretically detect text regions, it is not optimized for text extraction and requires extensive training data, whereas OCR is a prebuilt, ready-to-use feature.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Image Analysis (which can detect text in images via the 'tags' or 'description' features) with the dedicated OCR capability, but Image Analysis does not provide the precise text extraction and bounding box coordinates that OCR offers.
Detailed technical explanation
How to think about this question
Azure's OCR engine uses deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with connectionist temporal classification (CTC), to recognize both printed and handwritten text. The Read API, which is the latest OCR offering, supports asynchronous processing for large documents and can handle varied handwriting styles, though accuracy may degrade with highly cursive or poorly legible text. In a real-world logistics scenario, OCR would be combined with post-processing (e.g., regex or NLP) to parse extracted text into structured fields like address and weight.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 A, Optical Character Recognition (OCR), because the task requires extracting text (destination address, sender name, package weight) from handwritten shipping forms. Azure's OCR API, part of Computer Vision, is specifically designed to detect and read printed and handwritten text from images, making it the appropriate capability for this document processing 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
3 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A logistics company receives thousands of handwritten shipping labels daily. They need an automated solution to extract the destination address, sender name, and package weight from these labels. Which prebuilt Azure Computer Vision capability should they use?
easy- ✓ A.Optical Character Recognition (OCR)
- B.Object detection
- C.Image classification
- D.Facial recognition
Why A: Option A is correct because Azure Computer Vision's Optical Character Recognition (OCR) API is specifically designed to extract printed or handwritten text from images. In this scenario, the handwritten shipping labels contain textual data (destination address, sender name, package weight), and OCR can read and digitize that text for automated processing. The other options address different visual tasks—object detection, classification, or facial recognition—none of which extract text content.
Variation 2. 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?
easy- A.Image Analysis (descriptions and tags)
- ✓ B.Optical Character Recognition (OCR)
- C.Object Detection
- D.Face API
Why B: 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.
Variation 3. A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?
easy- A.Image classification
- ✓ B.Optical character recognition (OCR)
- C.Object detection
- D.Face detection
Why B: Optical character recognition (OCR) is the correct Azure Cognitive Services capability because it is specifically designed to extract printed or handwritten text from images and convert it into machine-readable digital text. In this scenario, the logistics company needs to read handwritten addresses from shipping labels, which is a classic OCR workload. Azure's Computer Vision OCR API (including the Read API) can handle both printed and handwritten text, making it the ideal choice for this task.
Last reviewed: Jun 11, 2026
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