- A
Image classification
Why wrong: Image classification assigns a single label to the entire image (e.g., 'container yard'), not individual objects or their instances.
- B
Object detection
Why wrong: Object detection draws bounding boxes around objects. For overlapping containers, bounding boxes may merge or fail to separate individual containers, reducing counting accuracy.
- C
Instance segmentation
Instance segmentation identifies each object instance separately and produces a pixel-level mask for each, enabling accurate counting even when objects overlap.
- D
Semantic segmentation
Why wrong: Semantic segmentation classifies every pixel into a category (e.g., 'container') but does not differentiate between individual containers, so it cannot provide an instance count.
Quick Answer
The answer is instance segmentation. This is the correct choice because instance segmentation goes beyond simply classifying pixels as belonging to a general category—it creates a unique pixel-level mask for every individual object in the image, even when containers are stacked or overlapping. For the logistics company’s drone imagery, this means each shipping container gets its own distinct boundary, allowing the model to count them accurately despite partial occlusion. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the difference between semantic segmentation (which groups all containers into one class mask) and instance segmentation (which separates each instance). A common trap is confusing the two: semantic segmentation would paint all containers the same color, failing to separate them. Remember the memory tip: “Semantic = Same class, Instance = Individual.”
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.
A logistics company uses drone imagery to monitor a busy container yard. They need to count the exact number of individual shipping containers, even when containers are partially stacked on top of each other or overlapping in the image. Which Azure Computer Vision capability should they choose to achieve the most accurate individual object separation?
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 is the correct choice because it not only detects each individual object in an image but also generates a pixel-level mask for each instance, allowing the model to distinguish between overlapping or stacked objects like shipping containers. This capability provides the most accurate separation of individual containers, even when they partially occlude each other, by assigning unique masks to each instance rather than grouping all containers into a single class.
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 the entire image (e.g., 'container yard'), not individual objects or their instances.
- ✗
Object detection
Why it's wrong here
Object detection draws bounding boxes around objects. For overlapping containers, bounding boxes may merge or fail to separate individual containers, reducing counting accuracy.
- ✓
Instance segmentation
Why this is correct
Instance segmentation identifies each object instance separately and produces a pixel-level mask for each, enabling accurate counting even when objects overlap.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel into a category (e.g., 'container') but does not differentiate between individual containers, so it cannot provide an instance count.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse semantic segmentation (which labels all pixels of a class as one group) with instance segmentation (which separates individual objects), leading them to pick D when they need per-object counting.
Detailed technical explanation
How to think about this question
Instance segmentation models, such as Mask R-CNN, combine object detection (region proposal networks) with a parallel branch for predicting segmentation masks, enabling per-pixel instance differentiation. In a container yard scenario, this allows the model to output a unique mask for each container even when they are partially stacked, as the mask head learns to separate instances based on spatial and feature cues. A subtle behavior is that instance segmentation can handle varying degrees of occlusion by leveraging non-maximum suppression on masks, not just bounding boxes, which improves accuracy in dense scenes.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 is the correct choice because it not only detects each individual object in an image but also generates a pixel-level mask for each instance, allowing the model to distinguish between overlapping or stacked objects like shipping containers. This capability provides the most accurate separation of individual containers, even when they partially occlude each other, by assigning unique masks to each instance rather than grouping all containers into a single class.
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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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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