The answer is Azure AI Custom Vision. This service is the correct choice because it is purpose-built for training a custom image classification model using your own labeled images, leveraging transfer learning to fine-tune a pre-trained neural network on domain-specific visual concepts. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between pre-built AI services (like Azure AI Vision, which offers general image analysis) and customizable services like Custom Vision, which allows you to upload, tag, and train a model from scratch. A common trap is confusing Custom Vision with Azure AI Vision; remember that “Custom” in the name signals you bring your own labeled data. For a quick memory tip, think “Custom = your own categories, Vision = pre-built categories.”
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.
Which Azure AI service enables you to train a custom image classification model with your own labeled images?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Custom Vision
Azure AI Custom Vision (option B) is the correct service because it is specifically designed to allow users to upload their own labeled images, train a custom image classification model, and then deploy it via a REST API endpoint. Unlike the pre-built Azure AI Vision service, Custom Vision provides the ability to fine-tune a model on domain-specific visual concepts using transfer learning, making it ideal for bespoke classification tasks.
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 AI Vision (pre-built)
Why it's wrong here
Azure AI Vision provides pre-built models — Custom Vision is the service for training custom models with your own labeled images.
✓
Azure AI Custom Vision
Why this is correct
Custom Vision lets you train custom image classification and object detection models by uploading and labeling your own images.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Machine Learning
Why it's wrong here
Azure ML is a full ML platform; Custom Vision is the simpler, no-code service specifically for custom vision model training.
✗
Azure AI Face
Why it's wrong here
Azure AI Face detects and analyzes faces — it doesn't train custom image classifiers from user-provided images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the pre-built Azure AI Vision service (which cannot be retrained) with the Custom Vision service, assuming that 'AI Vision' includes custom training capabilities, when in fact Custom Vision is a separate Azure resource with a distinct training workflow.
Detailed technical explanation
How to think about this question
Under the hood, Azure Custom Vision uses transfer learning from a pre-trained deep neural network (e.g., ResNet or MobileNet) and fine-tunes the final layers on your labeled dataset, which dramatically reduces the amount of training data and time required compared to training from scratch. The service automatically handles data augmentation, hyperparameter tuning, and model evaluation, exposing a simple training API and a prediction endpoint that returns confidence scores for each class. A real-world scenario is training a model to classify different types of industrial defects on a production line, where pre-built vision models would fail because they lack 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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 AI Custom Vision — Azure AI Custom Vision (option B) is the correct service because it is specifically designed to allow users to upload their own labeled images, train a custom image classification model, and then deploy it via a REST API endpoint. Unlike the pre-built Azure AI Vision service, Custom Vision provides the ability to fine-tune a model on domain-specific visual concepts using transfer learning, making it ideal for bespoke classification tasks.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'image classification' in Azure AI Custom Vision?
easy
A.Organising image files into folders on Azure Blob Storage by date
✓ B.Assigning a category label to an entire image based on its dominant visual content
C.Converting colour images to black and white for accessibility purposes
D.Sorting images by their file size and resolution metadata
Why B: Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.
Last reviewed: Jun 11, 2026
Question Discussion
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