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| Vendor: | Microsoft |
|---|---|
| Exam Code: | AI-102 |
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Questions: | 381 |
| Last Updated: | April 7, 2026 |
| Related Certifications: | Azure AI Engineer Associate |
| Exam Tags: | Artificial Intelligence certifications, Microsoft Azure certifications Intermediate Azure AI Engineer |
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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You have a chatbot that uses question answering in Azure Cognitive Service for Language
Users report that the responses of the chatbot lack formality when answering spurious questions
You need to ensure that the chatbot provides formal responses to spurious questions.
Solution: From Language Studio, you change the chitchat source to qna_chitchat_professional.tsv. and then retrain and republish the model.
Does this meet the goal?
You develop a custom question answering project in Azure Cognitive Service for Language. The project will be used by a chatbot. You need to configure the project to engage in multi-turn conversations. What should you do?
You are building a Language Understanding model for an e-commerce chatbot. Users can speak or type their billing address when prompted by the chatbot.
You need to construct an entity to capture billing addresses.
Which entity type should you use?
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-entity-types
ML Entity with Structure
An ML entity can be composed of smaller sub-entities, each of which can have its own properties. For example, Address could have the following structure:
Address: 4567 Main Street, NY, 98052, USA
Building Number: 4567
Street Name: Main Street
State: NY
Zip Code: 98052
Country: USA
You are building a solution that will detect anomalies in sensor data from the previous 24 hours.
You need to ensure that the solution scans the entire dataset, at the same time, for anomalies.
Which type of detection should you use?
You are building a social media extension that will convert text to speech. The solution must meet the following requirements:
* Support messages of up to 400 characters.
* Provide users with multiple voice options.
* Minimize costs.
You create an Azure Cognitive Services resource.
Which Speech API endpoint provides users with the available voice options?
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