- A
A) Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images. Inventory shelf levels are not represented as text, so OCR is not applicable.
- B
B) Object detection
Why wrong: Object detection identifies the location of specific objects (e.g., boxes). While it could count items, the requirement is to classify the whole shelf's fill level, not detect individual objects.
- C
C) Image classification
Image classification assigns a category to the entire image. It is ideal for determining whether a shelf is full, half full, or empty based on the overall visual content.
- D
D) Semantic segmentation
Why wrong: Semantic segmentation labels each pixel in the image, which provides detailed region-level information. However, for simple shelf-level categorization, image classification is more straightforward and sufficient.
Quick Answer
The correct choice is image classification because the warehouse system needs to assign a single label—'full', 'half full', or 'empty'—to the entire image of a shelf. Image classification analyzes the whole image as one unit and outputs a category or tag, which directly matches this use case. In contrast, object detection would locate and label multiple individual items within the shelf, and semantic segmentation would label every pixel, both of which are unnecessary for determining the shelf’s overall state. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between core Computer Vision capabilities: image classification for whole-image categorization, object detection for locating objects, and semantic segmentation for pixel-level detail. A common trap is confusing image classification with object detection when the task involves analyzing a scene’s overall condition rather than identifying specific objects. Memory tip: think “one image, one label” for image classification—if the output is a single category for the entire picture, it’s classification.
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 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?
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.
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.
- ✗
A) Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images. Inventory shelf levels are not represented as text, so OCR is not applicable.
- ✗
B) Object detection
Why it's wrong here
Object detection identifies the location of specific objects (e.g., boxes). While it could count items, the requirement is to classify the whole shelf's fill level, not detect individual objects.
- ✓
C) Image classification
Why this is correct
Image classification assigns a category to the entire image. It is ideal for determining whether a shelf is full, half full, or empty based on the overall visual content.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
D) Semantic segmentation
Why it's wrong here
Semantic segmentation labels each pixel in the image, which provides detailed region-level information. However, for simple shelf-level categorization, image classification is more straightforward and sufficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Image classification in Azure Computer Vision uses a pre-trained deep neural network (e.g., ResNet or DenseNet) that processes the entire image through convolutional layers to extract features and outputs a probability distribution across predefined classes. The model is trained on millions of labeled images, and for custom scenarios like shelf fullness, you would use Custom Vision to fine-tune a classifier with your own labeled images. A subtle behavior is that image classification assumes a single dominant subject in the image, so if a shelf contains a mix of items, the model may struggle unless trained with diverse examples.
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: 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.
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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