AI-102 Implement computer vision solutions Practice Question
Exhibit
{
"customvision": {
"project": {
"name": "DefectDetection",
"type": "ObjectDetection",
"domain": "General",
"exportable": true
},
"training": {
"iteration": {
"name": "Iteration 5",
"publishName": "defect-model",
"status": "Completed",
"performance": {
"precision": 0.85,
"recall": 0.78,
"mAP": 0.82
}
}
}
}
}Refer to the exhibit. You have trained an object detection model in Azure Custom Vision. The model is published as 'defect-model'. You need to deploy this model to a Docker container for on-premises inference using the Azure IoT Edge runtime. What should you do first?
⚠ Common exam trap
It's easy for candidates to think they can directly use the cloud prediction endpoint on an edge device, but Azure IoT Edge requires a containerized module for local execution, making the export step mandatory before any deployment.
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
✓
Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.
To deploy a Custom Vision model to an Azure IoT Edge device, you must first export the model as a Docker container (e.g., TensorFlow, ONNX, or DockerFile) from the Custom Vision portal. This export creates a container image that can be deployed to Azure Container Registry and then used as a module in an IoT Edge deployment. Without this export step, you cannot create the containerized module required for on-premises inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an Azure Container Registry and push the Custom Vision base image.
Why it's wrong here
You need to export the model first.
- ✓
Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.
Why this is correct
Exporting creates a container image for offline inference.
- ✗
Use the Custom Vision prediction API to call the published endpoint from the edge device.
Why it's wrong here
The prediction API requires internet connectivity.
- ✗
Retrain the model with more images to improve mAP.
Why it's wrong here
Retraining is not required for deployment.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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