AI-900 Practice Question: Describe features of computer vision workloads on Azure
A transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?
⚠ Common exam trap
A common mix-up: candidates confuse object detection (which finds and labels multiple objects) with image classification (which labels the whole image), especially when the question mentions multiple vehicle types, leading them to incorrectly choose object detection.
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
✓
Image classification
Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Object detection
Why it's wrong here
Object detection is not the right fit because it provides a bounding box and a class label for each detected object, and it is designed to find multiple objects within one image. The transportation scenario only asks whether the vehicle type can be identified from the whole image, not where the vehicle is located or how many vehicles appear. Choosing detection would add unnecessary spatial output and require more complex post-processing to collapse the results into a single image-level answer.
When this WOULD be correct
A company wants to detect and locate specific types of vehicles (e.g., cars, trucks) in an image, drawing bounding boxes around each vehicle. The system must output the positions and labels of all vehicles present.
- ✓
Image classification
Why this is correct
Image classification is the correct choice because it maps an entire input image to a single semantic label, such as 'delivery truck' or 'sedan', by evaluating the dominant visual features. The model is trained on labeled vehicle photos and outputs a probability distribution over the possible vehicle types, letting the transportation company quickly determine whether an image shows a particular category. This aligns directly with the requirement to identify the vehicle type without needing to localize objects or annotate individual pixels.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is not relevant because it extracts text characters from images, such as license plate numbers, road signs, or printed labels, rather than identifying physical objects by their shape and appearance. While OCR might read a logo or text on a vehicle, it does not understand the vehicle's body type, number of wheels, or other visual characteristics needed to classify it. The intended use case is visual recognition of vehicle morphology, not text extraction.
When this WOULD be correct
A company needs to automatically read license plate numbers from images of vehicles. The system should output the alphanumeric characters on the plate. OCR would be the correct capability to extract text from images.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation is unsuitable because it applies a class label to every pixel in the image, generating a dense segmentation map rather than a single overall label. This pixel-level detail is useful for autonomous driving or lane detection, but it is far more granular than a transportation company's need to simply identify the type of vehicle shown. Segmentation also emphasizes spatial boundaries and scene composition, so it would not directly answer whether an image is a certain vehicle category without additional aggregation logic.
When this WOULD be correct
A question asking for a system that identifies the exact shape and location of each vehicle in an image, such as 'Which Azure service should be used to precisely outline every car, truck, and motorcycle in an image?' would make semantic segmentation correct.
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.
✓Image classificationCorrect answer▾
Why this is correct
Image classification is the correct choice because it maps an entire input image to a single semantic label, such as 'delivery truck' or 'sedan', by evaluating the dominant visual features. The model is trained on labeled vehicle photos and outputs a probability distribution over the possible vehicle types, letting the transportation company quickly determine whether an image shows a particular category. This aligns directly with the requirement to identify the vehicle type without needing to localize objects or annotate individual pixels.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and localizes multiple objects within an image with bounding boxes, but the question requires a single label for the entire image, not multiple labels or locations.
★ When this WOULD be the correct answer
A company wants to detect and locate specific types of vehicles (e.g., cars, trucks) in an image, drawing bounding boxes around each vehicle. The system must output the positions and labels of all vehicles present.
Why candidates choose this
Candidates may confuse object detection with image classification because both involve identifying objects, but object detection provides more detail (location) than needed here.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR is designed to extract text from images, not to classify the type of vehicle (car, truck, motorcycle) in an image. The question requires identifying the object category for the entire image, which is image classification, not text recognition.
★ When this WOULD be the correct answer
A company needs to automatically read license plate numbers from images of vehicles. The system should output the alphanumeric characters on the plate. OCR would be the correct capability to extract text from images.
Why candidates choose this
Candidates may confuse OCR with general image recognition because both involve analyzing image content, leading them to think OCR can identify vehicle types when it only handles text.
✗Semantic segmentationWrong answer — click to see why▾
Why this is wrong here
Semantic segmentation assigns a label to every pixel in the image, not a single label for the entire image. The requirement is to output one label per image, which is image classification.
★ When this WOULD be the correct answer
A question asking for a system that identifies the exact shape and location of each vehicle in an image, such as 'Which Azure service should be used to precisely outline every car, truck, and motorcycle in an image?' would make semantic segmentation correct.
Why candidates choose this
Candidates may confuse semantic segmentation with image classification because both involve labeling, but segmentation provides pixel-level detail, which seems more powerful for identifying multiple objects.
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
Image classification
Image classification is the process of teaching a computer to look at a picture and decide what category or label best describes the main object or scene in that picture.
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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