AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
Microsoft often tests the distinction between OCR and image classification, where candidates mistakenly choose image classification because they think 'reading text' is a form of classifying the image content, but OCR is a specialized text extraction service, not a classification task.
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)
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.
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 classification
Why it's wrong here
Image classification assigns a single categorical label (for example, 'shipping label' or 'document') to the entire image based on its overall visual content. It uses object-level patterns to distinguish image types, but it does not localize or transcribe individual characters, words, or lines. Classifying a label as a 'shipping label' would not reveal the handwritten destination, so it is insufficient for extracting shipping details.
When this WOULD be correct
A company wants to automatically sort images of shipping labels into categories such as 'domestic' or 'international' based on the overall appearance of the label. Image classification would be the correct service to assign a category label to each image.
- ✓
Optical character recognition (OCR)
Why this is correct
Optical character recognition (OCR) directly extracts the textual characters from a shipping label image, converting handwritten names and addresses into machine-readable text. Azure AI Vision's Read API and OCR engines detect character shapes stroke-by-stroke, even for cursive or messy handwriting, and output the recognized words in a structured format. This is precisely what the logistics company needs to digitize thousands of handwritten labels.
- ✗
Object detection
Why it's wrong here
Object detection draws bounding boxes around physical entities in an image, such as parcels, barcodes, pallets, or people, and labels each with a class. It can potentially locate the region of a shipping label, but it does not perform any text recognition or character decoding. Since the logistics company needs the actual handwritten content, object detection cannot turn those bounding boxes into usable address data.
When this WOULD be correct
A warehouse management system needs to automatically detect and count packages of different sizes on a conveyor belt. Object detection would be the correct service to identify each package and its location in the image.
- ✗
Face detection
Why it's wrong here
Face detection identifies the spatial locations and bounding boxes of human faces in an image, optionally returning attributes like age or emotion, but it cannot interpret any written content. Handwritten shipping labels do not typically contain faces, and even if one appeared, face detection would ignore the text. Therefore, it provides no mechanism to extract the address or recipient information required by the logistics company.
When this WOULD be correct
A company wants to automatically detect and count the number of people entering a building from security camera footage. Face detection would be the correct Azure Cognitive Services capability to identify and locate faces in images.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Optical character recognition (OCR)Correct answer▾
Why this is correct
Optical character recognition (OCR) directly extracts the textual characters from a shipping label image, converting handwritten names and addresses into machine-readable text. Azure AI Vision's Read API and OCR engines detect character shapes stroke-by-stroke, even for cursive or messy handwriting, and output the recognized words in a structured format. This is precisely what the logistics company needs to digitize thousands of handwritten labels.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to an entire image (e.g., 'handwritten label'), but it cannot extract the specific text content from the addresses. The requirement is to read and digitize handwritten text, which requires OCR.
★ When this WOULD be the correct answer
A company wants to automatically sort images of shipping labels into categories such as 'domestic' or 'international' based on the overall appearance of the label. Image classification would be the correct service to assign a category label to each image.
Why candidates choose this
Candidates may think that 'classifying' the handwritten text is similar to reading it, not realizing that classification outputs a category, not the actual text characters.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and locates objects within an image (e.g., boxes, vehicles), but it does not extract text. The question specifically requires reading handwritten addresses, which is a text extraction task, not object localization.
★ When this WOULD be the correct answer
A warehouse management system needs to automatically detect and count packages of different sizes on a conveyor belt. Object detection would be the correct service to identify each package and its location in the image.
Why candidates choose this
Candidates may confuse object detection with OCR because both involve analyzing images, but object detection focuses on physical objects rather than text characters.
✗Face detectionWrong answer — click to see why▾
Why this is wrong here
Face detection identifies human faces in images, not text. The question requires reading handwritten addresses, which is a text recognition task, not face detection.
★ When this WOULD be the correct answer
A company wants to automatically detect and count the number of people entering a building from security camera footage. Face detection would be the correct Azure Cognitive Services capability to identify and locate faces in images.
Why candidates choose this
Candidates may confuse face detection with OCR because both involve analyzing images, but they serve different purposes. The term 'detection' might lead them to think it can detect text, but face detection is specifically for faces.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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