Deploy Custom Vision Model to Docker Container for Azure IoT Edge
This AI-102 practice question tests your understanding of implement computer vision solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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.
Option B is correct because 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.
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.
✗
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.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
When you export a Custom Vision model as a Docker container, the portal packages the trained weights, inference code, and dependencies into a Docker image that can run on platforms like TensorFlow Serving or ONNX Runtime. This container can be deployed to Azure IoT Edge as a custom module, enabling low-latency, offline inference on edge devices. The export process also generates a deployment manifest template that simplifies integration with the IoT Edge runtime.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal. — Option B is correct because 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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