The answer is Azure Custom Vision, because it enables the art gallery to train a custom image classifier using their library of high-quality painting images, allowing the mobile app to identify specific artworks from visitor photos and return detailed information. This service is designed for custom classification scenarios where pre-built models like those in Computer Vision cannot recognize unique objects such as individual paintings. On the AI-900 exam, this question tests your understanding of when to choose Custom Vision over other Azure AI services—a common trap is selecting Computer Vision, which only identifies generic objects, not custom ones. Remember the key distinction: if you need to identify your own specific items (like paintings, products, or logos), the answer is always Custom Vision. A helpful memory tip: “Custom for custom content”—if the objects are unique to your dataset, Custom Vision is the correct choice.
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 art gallery wants to build a mobile app that allows visitors to take a photo of a specific painting and receive detailed information about that artwork. The gallery has a library of high-quality images of each painting in their collection. Which Azure AI service should they use to build this identification capability?
Correct. Custom Vision enables you to train a custom image classifier using your own labeled images, which is exactly what the gallery needs to identify specific paintings.
B
Azure Computer Vision (pre-built image analysis)
Why wrong: Incorrect. The pre-built image analysis can describe objects and scenes but cannot recognize specific, custom objects like particular paintings without training.
C
Azure Face API
Why wrong: Incorrect. Face API is designed for detecting, recognizing, and analyzing human faces, not artworks.
D
Azure Computer Vision (OCR)
Why wrong: Incorrect. OCR extracts text from images; paintings may have text but the app needs to identify the artwork itself, not just any embedded text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Custom Vision
Azure Custom Vision is the correct choice because it allows the gallery to train a custom image classification model using their library of high-quality painting images. This service enables the app to identify specific artworks from user-captured photos and return detailed information, as it is designed for custom classification scenarios where pre-built models are insufficient.
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.
✓
Azure Custom Vision
Why this is correct
Correct. Custom Vision enables you to train a custom image classifier using your own labeled images, which is exactly what the gallery needs to identify specific paintings.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Computer Vision (pre-built image analysis)
Why it's wrong here
Incorrect. The pre-built image analysis can describe objects and scenes but cannot recognize specific, custom objects like particular paintings without training.
✗
Azure Face API
Why it's wrong here
Incorrect. Face API is designed for detecting, recognizing, and analyzing human faces, not artworks.
✗
Azure Computer Vision (OCR)
Why it's wrong here
Incorrect. OCR extracts text from images; paintings may have text but the app needs to identify the artwork itself, not just any embedded text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Azure Computer Vision's pre-built image analysis with Custom Vision, assuming the former can be customized for specific objects, but only Custom Vision supports training on custom datasets.
Detailed technical explanation
How to think about this question
Azure Custom Vision uses transfer learning on a base model (e.g., ResNet) to fine-tune classification for custom categories. The gallery would upload labeled images of each painting, train a model, and export it as a Docker container or use the prediction API. A real-world scenario involves a museum app that identifies artworks with high accuracy even under varying lighting or angles, leveraging Custom Vision's ability to handle domain-specific visual features.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Azure Custom Vision — Azure Custom Vision is the correct choice because it allows the gallery to train a custom image classification model using their library of high-quality painting images. This service enables the app to identify specific artworks from user-captured photos and return detailed information, as it is designed for custom classification scenarios where pre-built models are insufficient.
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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Variation 1. A museum wants to create an app that allows visitors to take a photo of a painting and receive information about the artist, year, and style. The app needs to identify the painting from a database of thousands of artworks. Which Azure Computer Vision capability is most suitable?
medium
A.Optical Character Recognition (OCR)
✓ B.Image classification
C.Object detection
D.Face detection
Why B: Image classification is the correct choice because the app needs to assign a single label (the specific painting) to the entire photo. Azure Computer Vision's image classification models are trained to recognize and categorize entire images into predefined classes, which matches the requirement of identifying a painting from a database of thousands of artworks based on the visual content of the photo.
Last reviewed: Jun 11, 2026
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