AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
Test-takers frequently 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.
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 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.
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 discrete objects and localizes them with rectangular bounding boxes and class labels, but it does not assign a label to every pixel. For an MRI tumor, the output would be a coarse box that includes healthy tissue and fails to trace the irregular, fuzzy margin of the lesion. Precise boundary measurement and volume calculation require pixel-level delineation, so object detection is insufficient for the research team's goal.
- ✗
Image Classification
Why it's wrong here
Image classification assigns a single global label to an entire image, such as 'contains tumor' or 'healthy,' without any spatial localization. It produces no pixel-level output, so it cannot reveal the tumor's shape, size, or exact boundaries. The team needs a dense per-pixel map to analyze the MRI scans, making image classification far too coarse.
- ✓
Semantic Segmentation
Why this is correct
Semantic segmentation performs dense pixel-wise classification, assigning every pixel (or voxel) in the image to a class such as 'tumor' or 'healthy tissue.' This produces a high-resolution mask that precisely outlines the tumor boundary, enabling exact area and volume measurements. It is the standard computer vision task for anatomical delineation in radiology, and it aligns perfectly with the research team's requirement to analyze MRI scans for detailed boundaries.
- ✗
Optical Character Recognition
Why it's wrong here
Optical character recognition (OCR) extracts machine-printed or handwritten text from images by detecting characters and words. MRI scans are grayscale medical images without textual characters, and tumor boundaries are purely spatial/visual features unrelated to text. OCR operates in a fundamentally different domain and provides no mechanism to analyze tumor extent or boundaries.
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Related to this question
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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
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
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