AI-103 Text Analysis Practice Question
You are implementing a text classification pipeline using Azure AI Language. You want to deploy your trained custom model to a production endpoint. What is required before you can assign traffic to the model deployment in Language Studio?
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
✓
You must successfully complete model training and create a deployment in Language Studio.
Before a custom model can be deployed and used for inference, it must be successfully trained, and then assigned to a deployment name.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
You must successfully complete model training and create a deployment in Language Studio.
Why this is correct
Deploying a custom model requires a trained model and an active deployment name.
- ✗
You must export the model container and deploy it to an Azure Kubernetes Service (AKS) cluster.
Why it's wrong here
Azure AI Language manages model hosting natively in the cloud; container export is not required for standard cloud endpoints.
- ✗
You must submit a support ticket to Microsoft to enable custom model endpoints.
Why it's wrong here
Custom endpoints are available out-of-the-box on Standard tiers without Microsoft support intervention.
- ✗
You must convert the model files to ONNX format.
Why it's wrong here
Model conversion to ONNX is handled internally by the managed service.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 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-103 exam.