The answer is semantic segmentation, which is the correct Azure Computer Vision capability for pixel-level tumor boundary detection in MRI scans. This technique assigns a class label to every single pixel in an image, allowing the research team to precisely delineate tumor, healthy tissue, and background at the finest granularity. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the key difference between image classification (labels the whole image), object detection (draws bounding boxes around objects), and semantic segmentation (labels every pixel). A common trap is confusing semantic segmentation with instance segmentation—remember that semantic segmentation treats all instances of the same class as one region, while instance segmentation separates individual objects. For a memory tip, think of "pixel-perfect precision": if the task requires drawing exact boundaries at the pixel level, semantic segmentation is your go-to capability.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 medical research team wants to analyze MRI scans to identify and measure the precise boundaries of tumors. They need to assign each pixel in the image to a class (e.g., tumor, healthy tissue, background). Which Azure Computer Vision capability should they use?
Why wrong: Object detection identifies objects and their bounding boxes (rectangular regions), but does not assign a class to every pixel, which is required for precise boundary measurement.
B
Image Classification
Why wrong: Image classification assigns a label to the entire image, not to individual pixels. It cannot provide pixel-level boundaries for tumors.
C
Semantic Segmentation
Semantic segmentation classifies every pixel, producing a detailed map of regions (e.g., tumor boundaries), which is exactly what the research team needs.
D
Optical Character Recognition
Why wrong: OCR extracts text from images, not relevant for analyzing tumor boundaries in MRI scans.
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 assigns a class label to every pixel in an image, making it the correct choice for precisely delineating tumor boundaries in MRI scans. Azure Computer Vision's semantic segmentation capability outputs a pixel-level mask, enabling the research team to differentiate tumor, healthy tissue, and background at the finest granularity.
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 identifies objects and their bounding boxes (rectangular regions), but does not assign a class to every pixel, which is required for precise boundary measurement.
✗
Image Classification
Why it's wrong here
Image classification assigns a label to the entire image, not to individual pixels. It cannot provide pixel-level boundaries for tumors.
✓
Semantic Segmentation
Why this is correct
Semantic segmentation classifies every pixel, producing a detailed map of regions (e.g., tumor boundaries), which is exactly what the research team needs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition
Why it's wrong here
OCR extracts text from images, not relevant for analyzing tumor boundaries in MRI scans.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse object detection with segmentation, assuming bounding boxes are sufficient for boundary measurement, but Azure explicitly tests that semantic segmentation provides pixel-level precision required for medical imaging tasks.
Detailed technical explanation
How to think about this question
Semantic segmentation in Azure Computer Vision uses fully convolutional networks (FCNs) or U-Net architectures that output a dense prediction map where each pixel is classified. The model is trained on pixel-level annotated datasets, and the output is a mask with the same dimensions as the input image, allowing precise boundary delineation. In practice, this capability is critical for medical applications like tumor volume calculation, where even a few pixels of error can significantly affect treatment planning.
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 assigns a class label to every pixel in an image, making it the correct choice for precisely delineating tumor boundaries in MRI scans. Azure Computer Vision's semantic segmentation capability outputs a pixel-level mask, enabling the research team to differentiate tumor, healthy tissue, and background at the finest granularity.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A medical research team needs to analyze CT scans to identify and outline the exact boundaries of lung nodules. Which Azure Computer Vision capability should they use?
hard
A.Image Classification
B.Object Detection
✓ C.Semantic Segmentation
D.Optical Character Recognition (OCR)
Why C: Semantic segmentation is the correct capability because it classifies each pixel in an image, enabling precise delineation of object boundaries. For CT scans, this allows the model to outline the exact shape and contour of lung nodules, which is essential for medical analysis. Image classification and object detection only provide labels or bounding boxes, not pixel-level boundaries.
Variation 2. 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?
easy
A.Object Detection
✓ B.Semantic Segmentation
C.Image Classification
D.Optical Character Recognition (OCR)
Why B: 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.
Last reviewed: Jun 11, 2026
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