Learn the step-by-step process to deploy and integrate Azure AI’s Conversational Language Understanding (CLU) model into your web application for real-time customer support. Perfect for AI-102 exam preparation!
Table of Contents
Question
Your organization, Xerigon Corporation, has developed a natural language understanding (NLU) model using conversational language understanding (CLU) to process customer queries on a support platform. You want to integrate this model into a web application to provide real-time responses to user inquiries. The application needs to send user inputs to the deployed language model and receive intent predictions.
What is the first step in consuming the language model from the client application after you have created and trained it?
A. Configure the client application to use Azure Cognitive Search.
B. Deploy the model to the Azure cloud.
C. Use the model using REST API.
D. Use the client application to send prediction requests to the Azure AI Language service.
Answer
B. Deploy the model to the Azure cloud.
Explanation
After creating and training a language model, the first step to enable its use in client applications is to deploy it to the Azure cloud. Deploying the model makes it accessible via a specific prediction endpoint which is essential for sending requests from your application.
Using the model using REST API is not the first step after you have created and trained the language model. Once the model has been deployed, it can be accessed using REST API calls. The client application can send HTTP requests to the endpoint, passing user inputs and receiving intent predictions in response.
Configuring the client application to use Azure Cognitive Search is not the first step after you have created and trained the language model. Azure Cognitive Search is a separate service designed for indexing and searching structured data. It is not related to consuming a model from a client application.
Using the client application to send prediction requests to the Azure AI Language service is not the first step after you have created and trained the language model. Sending prediction requests to the Azure AI Language service is a critical part of consuming the model, but it is not the initial step. Before the client application can send requests, the model must first be deployed to generate the endpoint URL and prediction key required for making API calls.
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