AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Why wrong: Object Detection provides bounding boxes around objects, but does not offer pixel-level classification required to accurately draw room layouts.
B
Semantic Segmentation
Semantic Segmentation classifies each pixel in an image into predefined categories, making it ideal for identifying and outlining walls, doors, windows, and furniture.
C
Image Classification
Why wrong: Image Classification assigns a label to the entire image or identifies objects within it, but does not provide pixel-level boundaries or spatial layout.
D
Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images, which is unrelated to identifying structural elements or generating floor plans.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 provides bounding boxes around objects, but does not offer pixel-level classification required to accurately draw room layouts.
✓
Semantic Segmentation
Why this is correct
Semantic Segmentation classifies each pixel in an image into predefined categories, making it ideal for identifying and outlining walls, doors, windows, and furniture.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image Classification
Why it's wrong here
Image Classification assigns a label to the entire image or identifies objects within it, but does not provide pixel-level boundaries or spatial layout.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, which is unrelated to identifying structural elements or generating floor plans.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Semantic segmentation in Azure Computer Vision uses deep convolutional neural networks (e.g., fully convolutional networks or U-Net architectures) to output a dense prediction map where each pixel is assigned a class ID. Under the hood, the model applies atrous convolution and skip connections to preserve spatial resolution, enabling precise boundary detection even for irregular shapes like furniture. In a real-world scenario, this capability is critical for autonomous driving (e.g., segmenting road, vehicles, pedestrians) and medical imaging (e.g., tumor delineation), where bounding boxes are insufficient.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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