Azure Custom Vision is the correct choice because it enables you to train a custom image classification model using your own labeled dataset of bird species, making it ideal for fine-grained visual recognition tasks. Unlike the pre-built Computer Vision service, which recognizes general objects and scenes, Custom Vision allows you to define and distinguish between dozens or hundreds of visually similar categories—exactly what is needed to differentiate bird species from uploaded photos. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of when to choose a customizable service over a pre-trained one; a common trap is selecting Computer Vision Image Analysis, which cannot be retrained on your own data. Remember the key distinction: if the scenario provides labeled images and requires training a model for specific categories, the answer is always Custom Vision. For a quick memory tip, think “Custom for custom”—if you need to train on your own images, Custom Vision is the service.
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 nature conservation organization wants to create an app that automatically identifies different species of birds from photos uploaded by birdwatchers. They have thousands of labeled images of bird species. Which Azure service should they use to train a custom model?
Why wrong: Image Analysis offers prebuilt capabilities like tagging and object detection, but it cannot be trained on custom categories like specific bird species.
B
Azure Custom Vision
Custom Vision is designed for training custom image classification or object detection models using labeled images, perfect for identifying different bird species.
C
Azure Face API
Why wrong: Face API is specialized for human face detection, recognition, and analysis, not for general object or species identification.
D
Azure Form Recognizer
Why wrong: Form Recognizer extracts text and structured data from documents like forms and invoices, not from photos of birds.
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 you to train a custom image classification model using your own labeled dataset of bird species. Unlike the pre-built Computer Vision Image Analysis service, Custom Vision specializes in fine-grained classification tasks where you need to distinguish between dozens or hundreds of visually similar categories, such as different bird species.
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 Computer Vision Image Analysis
Why it's wrong here
Image Analysis offers prebuilt capabilities like tagging and object detection, but it cannot be trained on custom categories like specific bird species.
✓
Azure Custom Vision
Why this is correct
Custom Vision is designed for training custom image classification or object detection models using labeled images, perfect for identifying different bird species.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Face API
Why it's wrong here
Face API is specialized for human face detection, recognition, and analysis, not for general object or species identification.
✗
Azure Form Recognizer
Why it's wrong here
Form Recognizer extracts text and structured data from documents like forms and invoices, not from photos of birds.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the general-purpose Computer Vision Image Analysis (which cannot be retrained) with Custom Vision (which is specifically designed for custom classification), leading them to pick option A.
Detailed technical explanation
How to think about this question
Under the hood, Azure Custom Vision uses transfer learning with a deep neural network (e.g., ResNet or MobileNet) that is fine-tuned on your labeled images. The service supports up to 10,000 images per project in the free tier and automatically handles image augmentation to improve model robustness. In a real-world scenario, a conservation organization would export the trained model as a TensorFlow or ONNX file for offline inference on edge devices like camera traps.
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 you to train a custom image classification model using your own labeled dataset of bird species. Unlike the pre-built Computer Vision Image Analysis service, Custom Vision specializes in fine-grained classification tasks where you need to distinguish between dozens or hundreds of visually similar categories, such as different bird species.
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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Question Discussion
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