AI-900 Practice Question: Describe features of computer vision workloads on Azure
What does it mean to 'export' a model from Azure AI Custom Vision?
⚠ Common exam trap
Many candidates confuse 'export' with 'sharing' or 'moving' the model to another Azure service, when in fact export specifically means downloading a deployable file for offline/edge 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
✓
Downloading the trained model as a file for offline inference on edge devices
Exporting a model from Azure AI Custom Vision means downloading the trained model as a file (e.g., TensorFlow, ONNX, CoreML, or Docker container) so it can be run locally on edge devices without requiring an internet connection to the cloud API. This enables offline inference, reduced latency, and data privacy for scenarios like manufacturing or retail.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sharing the model configuration with other Azure subscriptions
Why it's wrong here
Export in Custom Vision creates a deployable artifact (ONNX, TensorFlow, CoreML, or Dockerfile) for local inference—not a configuration-sharing operation. Sharing a model or project across subscriptions is done with Azure role-based access control (RBAC) on the Custom Vision resource, which grants permissions to other principals. Exporting produces a file for edge deployment; it neither grants access nor transfers configuration to another tenant.
- ✓
Downloading the trained model as a file for offline inference on edge devices
Why this is correct
The trained model is bundled into a downloadable file in formats such as ONNX, TensorFlow Lite, or CoreML, allowing it to run inside an app or on an edge device without making cloud API calls. This enables low-latency, offline inference for scenarios like industrial inspection or mobile photo sorting, while protecting data privacy by keeping images on-premises. That is exactly what Custom Vision's Export command does.
- ✗
Moving the model from Custom Vision to Azure Machine Learning
Why it's wrong here
Moving a model to Azure Machine Learning requires registering the exported artifact or using the AML SDK/pipelines to manage the model lifecycle—it is not performed by the Custom Vision Export button. Export simply downloads a model file for edge deployment; it does not migrate the model into a different Azure service workspace. The two activities are separate workflows with different endpoints and tooling.
- ✗
Submitting the model for Microsoft certification review
Why it's wrong here
Custom Vision has no certification-review step when you export a model; the Export command immediately produces downloadable files for local deployment, not a compliance or approval workflow. Microsoft may have separate Responsible AI or service-specific review processes in other products, but none is invoked by exporting from Custom Vision. Thus this option describes a nonexistent operation for this service.
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