AI-900 Practice Question: Describe features of computer vision workloads on Azure
A parking management company uses cameras at the entrance and exit of a lot. They need to automatically read the license plate numbers of each car as it enters and exits. Which Azure Computer Vision capability is specifically designed for this task?
⚠ Common exam trap
Watch out — candidates often confuse object detection with OCR, thinking that detecting a license plate as an object is sufficient, but OCR is required to actually read the alphanumeric text on the plate.
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 Azure Computer Vision capability specifically designed to extract printed or handwritten text from images, including license plate numbers. In this scenario, the cameras capture images of cars entering and exiting, and OCR processes those images to read the alphanumeric characters on the license plates. This is the exact use case for OCR, as it can handle varied fonts, angles, and lighting conditions common in parking lot environments.
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 (Optical Character Recognition) is a purpose-built AI capability in Azure AI Vision that extracts printed and handwritten text from images, including the alphanumeric characters on a vehicle's license plate. The Read API pipeline detects text regions, classifies each character, and returns the string in a structured response, so it can identify the plate number accurately without any custom model training. Because the license plate is literally text, OCR is the correct and most efficient service for this task.
- ✗
Object detection
Why it's wrong here
Object detection models, such as those built with Azure Custom Vision or the tailored Vision Studio object detection capability, return bounding-box coordinates and class labels (for example, 'car' or 'license plate') but do not output the characters contained inside an object. Even if a detection model isolates the plate's rectangular region, it has no mechanism to decode the grayscale or color pixels into letters and digits. Therefore, object detection is useful for locating the vehicle or plate but cannot itself read the plate number.
When this WOULD be correct
A question asking: 'Which Computer Vision capability should be used to count the number of cars in a parking lot image?' would make object detection correct, as it can detect and count car instances.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single category label to the entire image (e.g., 'car entering facility' or 'vehicle at gate') based on the overall visual content, but it does not provide region-specific textual extraction. Even if a classifier correctly labels the image as containing a car, it cannot identify the exact license plate number, which requires reading localized characters rather than predicting an image class. To read the specific plate value, the image must be processed by an OCR service, not by a classification model.
When this WOULD be correct
A question asks: 'Which Azure Computer Vision capability should be used to categorize images of vehicles into types such as sedan, SUV, or truck?'
- ✗
Facial recognition
Why it's wrong here
Facial recognition is a biometric identity technique that analyzes unique facial landmarks such as distance between eyes, nose shape, and jawline to verify or identify a person, and it is not designed to interpret text or characters on any surface. Azure AI Face also requires a face to be present in the image; a car's license plate has no facial features and would simply yield no face detection, let alone a textual readout. Thus, facial recognition is irrelevant for reading a license plate number.
When this WOULD be correct
A question asking which Azure Computer Vision capability can identify a specific person entering a building by analyzing their face captured from a security camera feed.
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 (Optical Character Recognition) is a purpose-built AI capability in Azure AI Vision that extracts printed and handwritten text from images, including the alphanumeric characters on a vehicle's license plate. The Read API pipeline detects text regions, classifies each character, and returns the string in a structured response, so it can identify the plate number accurately without any custom model training. Because the license plate is literally text, OCR is the correct and most efficient service for this task.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and locates objects in an image (e.g., cars, pedestrians), but it does not read text. The task requires reading license plate numbers, which is a text extraction task, not object localization.
★ When this WOULD be the correct answer
A question asking: 'Which Computer Vision capability should be used to count the number of cars in a parking lot image?' would make object detection correct, as it can detect and count car instances.
Why candidates choose this
Candidates may think license plate reading involves detecting the plate as an object first, but OCR is the specific service for extracting text from images, not just locating objects.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to an entire image (e.g., 'car'), but cannot extract specific text like license plate numbers from the image.
★ When this WOULD be the correct answer
A question asks: 'Which Azure Computer Vision capability should be used to categorize images of vehicles into types such as sedan, SUV, or truck?'
Why candidates choose this
Candidates may confuse classifying the type of vehicle with reading the license plate, thinking both involve identifying cars in images.
✗Facial recognitionWrong answer — click to see why▾
Why this is wrong here
Facial recognition is designed to identify or verify individuals based on facial features, not to read text or alphanumeric characters on license plates.
★ When this WOULD be the correct answer
A question asking which Azure Computer Vision capability can identify a specific person entering a building by analyzing their face captured from a security camera feed.
Why candidates choose this
Candidates may confuse facial recognition with OCR because both involve analyzing images to extract information, but they serve different purposes—faces vs. text.
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
Azure Machine Learning Studio
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.
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.