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 drone needs to navigate a forest by identifying individual trees, including their exact shape and boundaries, to avoid colliding with branches. The drone also needs to distinguish between trees and other objects like rocks. Which Azure Computer Vision capability is best suited for this requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Image classification assigns a single label to an entire image (e.g., 'forest'), but does not identify where objects are or their shapes.
B
Object detection
Why wrong: Object detection locates objects with bounding boxes and classifies them. However, bounding boxes are rectangular and do not capture the exact shape of irregular objects like tree branches.
C
Semantic segmentation
Semantic segmentation labels each pixel with a class (e.g., 'tree', 'rock', 'sky'). This provides the precise shape and boundaries needed for collision avoidance.
D
Optical character recognition (OCR)
Why wrong: OCR extracts text from images. It is not used for identifying objects or their shapes.
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 choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.
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 single label to an entire image (e.g., 'forest'), but does not identify where objects are or their shapes.
✗
Object detection
Why it's wrong here
Object detection locates objects with bounding boxes and classifies them. However, bounding boxes are rectangular and do not capture the exact shape of irregular objects like tree branches.
✓
Semantic segmentation
Why this is correct
Semantic segmentation labels each pixel with a class (e.g., 'tree', 'rock', 'sky'). This provides the precise shape and boundaries needed for collision avoidance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical character recognition (OCR)
Why it's wrong here
OCR extracts text from images. It is not used for identifying objects or their shapes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection (bounding boxes) with semantic segmentation (pixel-level masks), assuming bounding boxes provide enough detail for precise boundary avoidance, but the question explicitly requires 'exact shape and boundaries,' which only pixel-level segmentation can deliver.
Detailed technical explanation
How to think about this question
Semantic segmentation uses fully convolutional networks (FCNs) or encoder-decoder architectures like U-Net to produce a dense prediction map where each pixel is classified. In Azure Custom Vision, semantic segmentation is not natively supported for custom models, but Azure Computer Vision's pre-built 'Segment' API (part of the Image Analysis 4.0) can perform background removal or common object segmentation; for custom pixel-level tasks, Azure Machine Learning with deep learning frameworks is typically used. A real-world scenario is autonomous driving, where semantic segmentation identifies road, sidewalk, vehicles, and pedestrians at the pixel level for safe navigation.
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 is the correct choice because it classifies every pixel in an image, assigning each pixel to a specific class (e.g., 'tree', 'rock', 'branch'). This pixel-level precision allows the drone to identify the exact shape and boundaries of individual trees, which is essential for collision avoidance in a forest environment.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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