AI-900 Practice Question: Describe features of computer vision workloads on Azure
Which Azure AI service enables you to train a custom image classification model with your own labeled images?
⚠ Common exam trap
A common mix-up: 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.
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
✓
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.
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 (pre-built) provides fixed, Microsoft-trained models for general image analysis tasks such as optical character recognition, thumbnail generation, and automatic tagging, but it does not allow you to upload your own labeled images to retrain those models. Although it can classify or tag content, it operates on a predefined taxonomy and cannot be adapted to custom categories. Therefore, it is not suitable for building a bespoke image classifier, which requires training on user-supplied data.
- ✓
Azure AI Custom Vision
Why this is correct
Azure AI Custom Vision is the correct choice because it is a purpose-built Azure Cognitive Services offering that lets you create, train, and publish custom image classification and object detection models using your own uploaded, labeled images. You can quickly define custom classes, train the model through the user interface or SDK, and iterate on training data without writing custom machine learning code. After training, it exposes a REST prediction endpoint for your application, making it the streamlined, out-of-the-box service for this scenario.
- ✗
Azure Machine Learning
Why it's wrong here
Azure Machine Learning is a comprehensive platform for building, training, and deploying machine learning models, including custom image classification. However, it is not the *specific* AI service designed for training custom image classification models with labelled images out-of-the-box. This option is tempting because Azure Machine Learning *can* be used for this purpose, but it requires more manual configuration and code-writing to set up the training pipeline compared to a dedicated image classification service.
- ✗
Azure AI Face
Why it's wrong here
Azure AI Face is a specialized Cognitive Service restricted to detecting, identifying, and analyzing human faces, including attributes like age, emotion, and face landmarks. Its models are pre-trained exclusively for facial features and cannot be retrained or repurposed to classify arbitrary objects or scenes from user-supplied labeled images. Thus, it fails for a general custom image classification task because its scope is narrowly tied to faces rather than open-ended categories.
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Machine Learning Core Concepts
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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.