AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
Many exam-takers confuse object detection with semantic segmentation, assuming bounding boxes are sufficient for boundary outlining, but the exam tests the distinction between rectangular region identification and pixel-level precision.
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 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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Classification
Why it's wrong here
Image classification assigns a single global label to the entire CT scan or slice, such as “nodule present” or “nodule absent.” It provides no spatial coordinates, no region outline, and no pixel mask, so it cannot identify where a nodule is located or delineate its boundary. Outlining requires a model that makes predictions at the pixel level, which image classification fundamentally lacks.
- ✗
Object Detection
Why it's wrong here
Object detection improves on image classification by drawing a bounding box around each nodule, giving the approximate location and size. However, a bounding box is a rectangular region that inevitably includes adjacent healthy tissue and cannot follow the irregular, spiculated contours of a real nodule. Because the task asks for precise outlines rather than coarse rectangle coordinates, object detection is insufficient for exact boundary delineation.
- ✓
Semantic Segmentation
Why this is correct
Semantic segmentation performs dense pixel-wise classification, assigning every pixel (or voxel in a 3D CT volume) to a semantic class such as “nodule” or “background.” The output is a segmentation mask in which each nodule’s boundary follows the exact shape visible in the image, enabling accurate measurements of diameter, volume, and shape. This pixel-level output is precisely what is required to outline lung nodules on CT scans.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is designed to recognize and digitize printed or handwritten text characters from images, such as extracting patient identifiers from scanned forms. CT scans are medical images that contain anatomical structures rather than text, so there are no characters or strings for OCR to interpret. Therefore, OCR has no role in identifying or outlining lung nodules and is irrelevant to this imaging task.
Go deeper
Related to this question
Learn chapter
Regression and Classification
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
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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