AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
A common mix-up: candidates confuse OCR with object detection, thinking that 'extracting' information from an image is the same as identifying objects, but OCR is the only service that reads text characters from images.
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)
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.
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 extracts text (including handwriting) from images, perfect for reading shipping labels.
- ✗
Object detection
Why it's wrong here
Object detection identifies and locates objects in images, not for reading text.
When this WOULD be correct
Object detection would be correct for a question like: 'A warehouse needs to identify and count packages of different sizes on a conveyor belt from camera images.'
- ✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image, not for extracting text.
When this WOULD be correct
A company needs to automatically sort incoming packages by type (e.g., fragile, oversized, standard) based on images of the packages. Image classification would assign each image to a predefined category.
- ✗
Facial recognition
Why it's wrong here
Facial recognition identifies or verifies individuals from their faces, irrelevant for text extraction.
When this WOULD be correct
A question asking for a solution to automatically identify the sender of a package by matching their face from a photo on the shipping label or a delivery confirmation image would make facial recognition the correct answer.
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
OCR extracts text (including handwriting) from images, perfect for reading shipping labels.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and locates objects within an image, but it cannot extract text content like addresses or names. The requirement is to read handwritten text, which is a text extraction task, not object localization.
★ When this WOULD be the correct answer
Object detection would be correct for a question like: 'A warehouse needs to identify and count packages of different sizes on a conveyor belt from camera images.'
Why candidates choose this
Candidates may confuse object detection with OCR because both involve analyzing image content, but object detection focuses on finding objects, not reading text.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to an entire image, but the task requires extracting multiple specific text fields (address, name, weight) from handwritten labels, which OCR is designed for.
★ When this WOULD be the correct answer
A company needs to automatically sort incoming packages by type (e.g., fragile, oversized, standard) based on images of the packages. Image classification would assign each image to a predefined category.
Why candidates choose this
Candidates may think 'classifying' the content of the label (e.g., identifying it as a shipping label) is the goal, confusing broad categorization with detailed text extraction.
✗Facial recognitionWrong answer — click to see why▾
Why this is wrong here
Facial recognition is designed to identify or verify individuals from images, not to extract text or structured data like addresses, names, or weights from handwritten labels.
★ When this WOULD be the correct answer
A question asking for a solution to automatically identify the sender of a package by matching their face from a photo on the shipping label or a delivery confirmation image would make facial recognition the correct answer.
Why candidates choose this
Candidates may confuse 'recognition' with 'reading' or think that facial recognition can be adapted to extract any information from images, not understanding its specific purpose for human 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
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.