The answer is instance segmentation, the correct Azure Computer Vision capability for generating precise pixel-level masks for each individual object instance. This technique uniquely combines object detection with semantic segmentation, allowing the system to not only locate every car and pedestrian but also draw exact boundaries around each one, even when they overlap or partially occlude each other. On the AI-900 exam, this question tests your understanding of how Azure’s pre-built vision models map to real-world scenarios like autonomous driving, where distinguishing between overlapping instances is critical for safe navigation. A common trap is confusing instance segmentation with semantic segmentation, which labels all pixels of a class (e.g., all “car” pixels) without separating individual objects. To remember: think of “instance” as “individual” — instance segmentation gives each object its own unique mask, like giving each person in a crowd their own outline.
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.
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?
Why wrong: Image classification assigns a label to the whole image, with no object localization or pixel masks.
B
Object detection
Why wrong: Object detection provides bounding boxes around objects but not pixel-level segmentation masks for individual instances.
C
Semantic segmentation
Why wrong: Semantic segmentation labels each pixel with a class (e.g., car, road) but does not distinguish between different instances of the same class.
D
Instance segmentation
Instance segmentation provides a separate segmentation mask for each object instance, enabling precise separation even when objects overlap.
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.
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.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the whole image, with no object localization or pixel masks.
✗
Object detection
Why it's wrong here
Object detection provides bounding boxes around objects but not pixel-level segmentation masks for individual instances.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation labels each pixel with a class (e.g., car, road) but does not distinguish between different instances of the same class.
✓
Instance segmentation
Why this is correct
Instance segmentation provides a separate segmentation mask for each object instance, enabling precise separation even when objects overlap.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Instance segmentation models, such as Mask R-CNN, extend Faster R-CNN by adding a parallel branch that predicts a binary mask for each detected region of interest (RoI) using a fully convolutional network. This allows the model to output both a bounding box and a pixel-level mask per instance, handling occlusion by assigning each pixel to a specific object ID. In autonomous driving, this is critical for tasks like tracking individual pedestrians across frames or planning a path around a partially hidden vehicle.
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: 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.
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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