AI-900 Practice Question: Describe features of computer vision workloads on Azure
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?
⚠ Common exam trap
Test-takers frequently 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.
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.
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 categorical label to the entire image, such as 'container yard', without any localization or object-level analysis. It cannot detect individual containers, identify their positions, or count them; it only describes the whole scene as one category. Therefore it is fundamentally unsuited for counting objects in an image.
When this WOULD be correct
A question asking to categorize an entire drone image as 'congested yard' vs 'clear yard' would make image classification correct, as it only needs a single label per image.
- ✗
Object detection
Why it's wrong here
Object detection draws axis-aligned bounding boxes around objects, but overlapping containers cause bounding boxes to merge or be suppressed by non-maximum suppression. For heavily occluded or closely packed objects, a single box may encompass several containers, or NMS may discard a valid box because its overlap with a neighbor exceeds the threshold. Thus, simply counting boxes yields an unreliable and often too-low count.
When this WOULD be correct
A question asks: 'Which Azure Computer Vision capability should be used to identify the presence and location of shipping containers in an image, without needing to separate overlapping instances?' Object detection would be correct for this scenario.
- ✓
Instance segmentation
Why this is correct
Instance segmentation is the correct choice because it performs pixel-level classification that distinguishes each individual object instance, not just object classes. The model outputs a segmentation mask for every detected object separately, so even when containers overlap or touch in busy drone imagery, each container is assigned its own unique mask. Counting is then a simple matter of counting the number of distinct instance masks, which yields the accurate count the logistics company needs.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation assigns every pixel to a class label (e.g., 'container') but does not differentiate between different examples of the same class. When multiple containers overlap in drone imagery, their pixels merge into one contiguous region or blob, making it impossible to tell how many separate containers are present. It lacks an 'instance id' or mask separation, so it cannot provide an instance count.
When this WOULD be correct
A question asks: 'A city planning department needs to analyze satellite imagery to calculate the total area covered by roads, buildings, and vegetation. Which Azure Computer Vision capability should they use?' Semantic segmentation would be correct because it assigns every pixel to a class (road, building, vegetation) without needing to separate individual objects.
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
Instance segmentation is the correct choice because it performs pixel-level classification that distinguishes each individual object instance, not just object classes. The model outputs a segmentation mask for every detected object separately, so even when containers overlap or touch in busy drone imagery, each container is assigned its own unique mask. Counting is then a simple matter of counting the number of distinct instance masks, which yields the accurate count the logistics company needs.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to the entire image, so it cannot count or separate individual objects, especially overlapping containers.
★ When this WOULD be the correct answer
A question asking to categorize an entire drone image as 'congested yard' vs 'clear yard' would make image classification correct, as it only needs a single label per image.
Why candidates choose this
Candidates may think 'classification' can identify containers, but they overlook that it doesn't provide per-object localization or separation.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection provides bounding boxes around objects, but it cannot separate individual instances when containers overlap or are stacked, as it treats overlapping detections as separate objects without pixel-level distinction.
★ When this WOULD be the correct answer
A question asks: 'Which Azure Computer Vision capability should be used to identify the presence and location of shipping containers in an image, without needing to separate overlapping instances?' Object detection would be correct for this scenario.
Why candidates choose this
Candidates may think object detection is sufficient for counting because it identifies objects, but they overlook that overlapping containers require instance-level segmentation to separate them accurately.
✗Semantic segmentationWrong answer — click to see why▾
Why this is wrong here
Semantic segmentation classifies each pixel into a category (e.g., 'container'), but does not distinguish between individual objects of the same class. Overlapping containers would be merged into a single blob, preventing accurate counting of individual containers.
★ When this WOULD be the correct answer
A question asks: 'A city planning department needs to analyze satellite imagery to calculate the total area covered by roads, buildings, and vegetation. Which Azure Computer Vision capability should they use?' Semantic segmentation would be correct because it assigns every pixel to a class (road, building, vegetation) without needing to separate individual objects.
Why candidates choose this
Candidates may confuse semantic segmentation with instance segmentation, thinking that 'segmenting' the image into regions automatically separates individual objects. They might also overestimate the capability of semantic segmentation to handle overlapping objects.
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
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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