AI-900 Practice Question: Describe features of computer vision workloads on Azure
An autonomous vehicle team needs a system that not only identifies objects like cars and pedestrians but also creates a precise pixel-level mask for each individual object instance, even when objects overlap. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse semantic segmentation (which labels every pixel by class but not by instance) with instance segmentation, leading them to choose Option C when the question explicitly requires per-instance masks for overlapping objects.
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
✓
Instance segmentation
Instance segmentation (Option D) is the correct choice because it combines object detection with semantic segmentation to identify each individual object instance and generate a precise pixel-level mask for it, even when objects overlap. This capability is essential for autonomous vehicles to distinguish between multiple cars or pedestrians that may partially occlude each other, enabling safe navigation.
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
It treats the entire image as a single entity, outputting one class label—such as 'car' or 'pedestrian'—with no spatial grounding whatsoever. For an autonomous vehicle, this cannot distinguish multiple objects in the scene, nor can it provide locations or shapes needed for navigation and collision avoidance. It fails the requirement of separating overlapping instances entirely because it produces no per-pixel or per-region output at all.
When this WOULD be correct
An exam question asking for a capability that labels an entire image as containing a 'car' or 'pedestrian' without needing object locations or pixel-level detail would make image classification correct.
- ✗
Object detection
Why it's wrong here
It localizes objects with axis-aligned bounding boxes and class labels, giving approximate position and size, but boxes inevitably include background pixels and cannot represent the actual shape of a vehicle or pedestrian. Overlapping bounding boxes of the same class are not separated at the pixel level, so the system cannot tell exactly which pixels belong to which object. For autonomous driving, this offers only coarse spatial cues, insufficient for the fine-grained instance boundaries needed in dense traffic.
When this WOULD be correct
A question asking for a system that identifies objects and provides their bounding box coordinates (e.g., for counting or localization) without requiring pixel-level segmentation would make object detection the correct answer.
- ✗
Semantic segmentation
Why it's wrong here
It performs dense pixel-wise classification, labeling every pixel as road, sky, car, vegetation, etc., which provides scene-level understanding of categories. Yet it merges all objects of the same class into a single label—two adjacent cars become one contiguous blob, and pedestrians touching each other merge into a single cluster. It therefore cannot count, separate, or track individual instances of the same class, which is essential when vehicles or people overlap or are closely spaced on the road.
When this WOULD be correct
A question asks: 'Which Azure Computer Vision capability should be used to classify every pixel in an image into predefined categories (e.g., road, sky, pedestrian) without distinguishing between individual objects of the same class?' In that case, semantic segmentation is correct.
- ✓
Instance segmentation
Why this is correct
It combines object detection with pixel-level segmentation, assigning a unique mask to each distinct object instance even when same-class objects overlap or touch. For a self-driving vehicle, this means every car, pedestrian, cyclist, etc., gets its own precise silhouette, enabling boundary estimation, separate tracking, and safe maneuvering. This directly supplies the instance-level separation the team needs for counting and localizing each object to a pixel-level accuracy.
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.
✓Instance segmentationCorrect answer▾
Why this is correct
It combines object detection with pixel-level segmentation, assigning a unique mask to each distinct object instance even when same-class objects overlap or touch. For a self-driving vehicle, this means every car, pedestrian, cyclist, etc., gets its own precise silhouette, enabling boundary estimation, separate tracking, and safe maneuvering. This directly supplies the instance-level separation the team needs for counting and localizing each object to a pixel-level accuracy.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, not identifying individual objects or creating pixel-level masks for instances, especially overlapping ones.
★ When this WOULD be the correct answer
An exam question asking for a capability that labels an entire image as containing a 'car' or 'pedestrian' without needing object locations or pixel-level detail would make image classification correct.
Why candidates choose this
Candidates may confuse image classification with more advanced segmentation tasks, thinking it can handle object identification at a granular level.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection provides bounding boxes around objects but does not generate pixel-level masks, which are required for precise instance separation, especially when objects overlap.
★ When this WOULD be the correct answer
A question asking for a system that identifies objects and provides their bounding box coordinates (e.g., for counting or localization) without requiring pixel-level segmentation would make object detection the correct answer.
Why candidates choose this
Candidates may confuse object detection with instance segmentation because both identify individual objects, overlooking the critical requirement for pixel-level masks.
✗Semantic segmentationWrong answer — click to see why▾
Why this is wrong here
Semantic segmentation assigns the same class label to all pixels of a given category (e.g., all cars), but does not distinguish between individual object instances. The question requires per-instance pixel masks, especially for overlapping objects, which is the defining feature of instance segmentation.
★ When this WOULD be the correct answer
A question asks: 'Which Azure Computer Vision capability should be used to classify every pixel in an image into predefined categories (e.g., road, sky, pedestrian) without distinguishing between individual objects of the same class?' In that case, semantic segmentation is correct.
Why candidates choose this
Candidates may confuse semantic segmentation with instance segmentation because both produce pixel-level masks. They might overlook the critical requirement of separating overlapping instances, assuming semantic segmentation can handle it.
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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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
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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