AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying a custom text classification model using Azure AI Language. The model must be retrained monthly with new labeled data. You need to automate the retraining process with minimal manual intervention. Which approach should you use?
⚠ Common exam trap
Test-takers frequently confuse Azure Machine Learning with Azure AI Language, thinking that Azure Machine Learning is the appropriate service for hosting and retraining custom text classification models, when in fact Azure AI Language provides its own REST API for this purpose and is the correct service for custom text classification.
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
✓
Use the Azure AI Language REST API to trigger training and deployment on a schedule using Azure Logic Apps
Azure Logic Apps can schedule HTTP requests to the Azure AI Language REST API to trigger training and deployment of a custom text classification model. This approach automates the monthly retraining process without manual intervention, using the API's capabilities for model creation, training, and deployment.
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 DevOps pipeline that manually retrains the model every month
Why it's wrong here
A pipeline that still requires someone to trigger or approve monthly retraining leaves manual intervention in place, contradicting the automation requirement. Pipelines are the right vehicle when you want repeatable, scheduled orchestration of build and deployment steps with human oversight at defined gates.
- ✗
Retrain the model manually using Language Studio each month
Why it's wrong here
Retraining by hand in Language Studio each month is exactly the manual intervention the scenario forbids, and it introduces human error and delay. Language Studio suits exploratory labelling, evaluation and one-off model builds rather than scheduled, unattended retraining cycles.
- ✓
Use the Azure AI Language REST API to trigger training and deployment on a schedule using Azure Logic Apps
Why this is correct
Scheduled REST API calls from Logic Apps trigger training and deployment without human input, satisfying the monthly retraining requirement with minimal manual intervention. The API exposes training and deployment operations directly, so the workflow can orchestrate both steps automatically.
- ✗
Use Azure Machine Learning to host the custom model and automate retraining
Why it's wrong here
Azure Machine Learning hosts and trains your own models; Azure AI Language custom text classification is a managed service retrained through its own training APIs, so AML adds an unnecessary hosting layer. AML is correct when you build, tune and deploy bespoke models needing custom compute and pipelines.
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