AI-900 Practice Question: Describe features of computer vision workloads on Azure
A warehouse uses ceiling-mounted cameras to monitor inventory shelves. The system needs to determine whether each shelf is 'full', 'half full', or 'empty' based on the entire image of the shelf. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Many exam-takers confuse 'object detection' (which finds and locates objects) with 'image classification' (which labels the entire image), leading them to choose object detection when the task is to assign a single category to the whole scene.
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
✓
C) Image classification
Image classification (C) is the correct choice because the system needs to assign a single label (full, half full, or empty) to the entire image of a shelf. Azure Computer Vision's image classification analyzes the whole image and outputs a single category or tag, which directly matches the requirement of determining the overall state of the shelf. Object detection would identify and locate multiple objects within the image, not classify the entire scene, and semantic segmentation would assign a label to every pixel, which is overkill for this task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, which assumes characters or labels are present in the input. Inventory shelf levels are not represented as text; the camera sees physical stock such as boxes, bins, or empty space, none of which have readable glyphs. Even if barcode labels exist, OCR would only digitize those labels, not infer whether the shelf is full or empty from the overall scene. The task demands a holistic visual judgment of fill level, not text recognition.
- ✗
B) Object detection
Why it's wrong here
Object detection identifies and localizes individual objects (e.g., each box or pallet) by drawing bounding boxes around them. While one could theoretically count detected objects to estimate fullness, the requirement is a single categorical classification for the whole shelf: full, half full, or empty. Object detection yields discrete object locations and counts, which are overly granular and would require an extra aggregation rule to map counts to a fill-level class. It also struggles with irregular bulk items or when objects are stacked tightly and overlap, making detection unreliable for this specific fill-level assessment.
- ✓
C) Image classification
Why this is correct
Image classification assigns a single label to the entire input image, which directly matches the need to categorize a whole shelf as full, half full, or empty. A convolutional neural network can learn visual patterns such as occupancy density, edge distributions, and empty-background proportions to predict the fill-level class. This approach is efficient because it produces one output per image without needing to localize or segment any individual object, making it the simplest and most appropriate vision technique for this scenario.
- ✗
D) Semantic segmentation
Why it's wrong here
Semantic segmentation assigns a class label to every pixel in an image, producing a pixel‑level mask. This fails the scenario because the system only needs a single categorical decision per shelf (full, half full, empty), not a per‑pixel map of objects or regions. It is tempting because segmentation can distinguish occupied from empty shelf space, but the requirement is a whole‑image classification, not pixel‑wise labelling. Semantic segmentation would be correct for tasks like identifying the exact shape and location of each item on a shelf.
Go deeper
Related to this question
Learn chapter
Regression and Classification
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
Inventory
Inventory is the complete list of hardware, software, and network assets that an organization owns or manages, tracked for maintenance, security, and budgeting purposes.
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