nerdexam
Microsoft

AI-102 · Question #234

You train a Conversational Azure AI Language Understanding model to understand the natural language input of users. You need to evaluate the accuracy of the model before deploying it. What are two…

The correct answer is A. From the language authoring REST endpoint, retrieve the model evaluation summary. C. From Language Studio, select Model performance. To evaluate the accuracy of a trained Conversational Language Understanding model before deployment, you can either retrieve the model evaluation summary via the language authoring REST endpoint or directly view model performance metrics in Language Studio.

Submitted by kim_seoul· Mar 30, 2026Implement Natural Language Processing solutions

Question

You train a Conversational Azure AI Language Understanding model to understand the natural language input of users. You need to evaluate the accuracy of the model before deploying it. What are two methods you can use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

Options

  • AFrom the language authoring REST endpoint, retrieve the model evaluation summary.
  • BFrom Language Studio, enable Active Learning, and then validate the utterances logged for
  • CFrom Language Studio, select Model performance.
  • DFrom the Azure portal, enable log collection in Log Analytics, and then analyze the logs.

How the community answered

(46 responses)
  • A
    70% (32)
  • B
    17% (8)
  • D
    13% (6)

Why each option

To evaluate the accuracy of a trained Conversational Language Understanding model before deployment, you can either retrieve the model evaluation summary via the language authoring REST endpoint or directly view model performance metrics in Language Studio.

AFrom the language authoring REST endpoint, retrieve the model evaluation summary.Correct

The Azure AI Language authoring REST API provides endpoints specifically for retrieving model evaluation details, including metrics like precision, recall, and F1-score, which are crucial for assessing model accuracy and can be accessed programmatically.

BFrom Language Studio, enable Active Learning, and then validate the utterances logged for

Active Learning in Language Studio is used for improving model performance over time by suggesting new utterances to label, not for a direct evaluation of the current model's accuracy before initial deployment.

CFrom Language Studio, select Model performance.Correct

Azure AI Language Studio includes a 'Model performance' section or similar functionality (often under 'Test model' or 'Evaluate') where users can visually inspect the model's accuracy, view detailed evaluation metrics, and identify areas for improvement before deployment.

DFrom the Azure portal, enable log collection in Log Analytics, and then analyze the logs.

Enabling log collection in Log Analytics from the Azure portal is for monitoring service usage and operational issues, not for directly evaluating the accuracy of an AI Language Understanding model's training performance.

Concept tested: Evaluating Conversational Language Understanding model accuracy

Source: https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/how-to/test-model

Topics

#Azure AI Language Understanding#Model evaluation#Language Studio#REST API

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