AI-900 Practice Question: Describe features of computer vision workloads on Azure
A real estate company wants to create an application that automatically generates floor plans from photographs of rooms. The application needs to identify and delineate every pixel in the image that corresponds to walls, doors, windows, and furniture. Which Azure Computer Vision capability should the company use?
⚠ Common exam trap
Many exam-takers confuse object detection (bounding boxes) with semantic segmentation (pixel-level masks), mistakenly thinking detection can delineate walls and doors, but only segmentation provides the per-pixel classification required for floor plan generation.
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
✓
Semantic Segmentation
Semantic segmentation is the correct choice because it classifies every pixel in an image into predefined categories (e.g., walls, doors, windows, furniture), producing a pixel-level mask. This is exactly what the application needs to delineate each structural element and object in the room photograph, enabling accurate floor plan generation.
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 predicts a bounding box and class label for each recognized object, such as 'sofa' or 'window,' but every box is a coarse axis-aligned rectangle. Walls and doorways are long, thin, irregular structures that do not fit inside tight rectangles, and overlapping boxes give no information about adjacency or connectivity between spaces. The result lacks the per-pixel boundaries required to trace rooms accurately.
When this WOULD be correct
Object Detection would be correct if the application only needed to locate and count objects (e.g., chairs, tables) in a room using bounding boxes, without requiring pixel-level delineation of structural elements like walls and doors.
- ✓
Semantic Segmentation
Why this is correct
Semantic Segmentation labels every pixel of the input image with a class such as wall, door, window, or furniture, producing a dense, pixel-aligned mask. This per-pixel classification is what lets the app reconstruct the precise boundaries and spatial layout needed to draw a floor plan. It goes beyond coarse object locations because it preserves irregular shapes, wall thicknesses, and doorway openings with pixel-level accuracy.
- ✗
Image Classification
Why it's wrong here
Image Classification assigns a single label or a set of class probabilities to the whole image, answering 'what is in this photo?' rather than 'where are the structural elements?' It might correctly call a photo 'living room,' but it cannot output pixel coordinates, wall lengths, or doorway positions. Without any spatial output, it cannot generate a measurable floor plan layout.
When this WOULD be correct
A scenario where the company only needs to categorize the room type (e.g., kitchen, bedroom) from a photo, without identifying specific objects or their boundaries.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR (Optical Character Recognition) detects and transcribes text characters within an image, returning strings such as room numbers, dimensions, or labels on a blueprint. Although a floor plan might contain text that OCR could read, the model has no understanding of walls, doors, or furniture shapes. Extracting text cannot produce a geometric or structural representation of the apartment layout.
When this WOULD be correct
A company needs to extract printed text from scanned property documents or signs in room photos to automate data entry. The question would ask for a capability to read text from 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.
✓Semantic SegmentationCorrect answer▾
Why this is correct
Semantic Segmentation labels every pixel of the input image with a class such as wall, door, window, or furniture, producing a dense, pixel-aligned mask. This per-pixel classification is what lets the app reconstruct the precise boundaries and spatial layout needed to draw a floor plan. It goes beyond coarse object locations because it preserves irregular shapes, wall thicknesses, and doorway openings with pixel-level accuracy.
✗Object DetectionWrong answer — click to see why▾
Why this is wrong here
Object Detection identifies and locates objects within an image using bounding boxes, but it does not classify every pixel. The requirement to delineate every pixel for walls, doors, windows, and furniture demands pixel-level classification, which is provided by Semantic Segmentation, not Object Detection.
★ When this WOULD be the correct answer
Object Detection would be correct if the application only needed to locate and count objects (e.g., chairs, tables) in a room using bounding boxes, without requiring pixel-level delineation of structural elements like walls and doors.
Why candidates choose this
Candidates may confuse Object Detection with Semantic Segmentation because both involve identifying objects in images, leading them to overlook the specific requirement for pixel-level delineation.
✗Image ClassificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, but the requirement is to delineate every pixel corresponding to walls, doors, windows, and furniture, which requires pixel-level segmentation.
★ When this WOULD be the correct answer
A scenario where the company only needs to categorize the room type (e.g., kitchen, bedroom) from a photo, without identifying specific objects or their boundaries.
Why candidates choose this
Candidates may confuse image classification with segmentation because both involve labeling, but classification lacks the spatial precision needed for pixel-level delineation.
✗Optical Character Recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR extracts text from images, but the requirement is to identify and delineate every pixel corresponding to walls, doors, windows, and furniture, which is a pixel-level classification task, not text recognition.
★ When this WOULD be the correct answer
A company needs to extract printed text from scanned property documents or signs in room photos to automate data entry. The question would ask for a capability to read text from images.
Why candidates choose this
Candidates may confuse 'recognizing objects in images' with 'recognizing text in images,' or think OCR can identify structural elements like doors and windows if they have labels.
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?”
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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