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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: | October 25, 2025 |
| Related Certifications: | Azure AI Engineer Associate |
| Exam Tags: | Artificial Intelligence certifications, Microsoft Azure certifications Intermediate Azure AI Engineer |
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You are developing an application that will use Azure Cognitive Search for internal documents.
You need to implement document-level filtering for Azure Cognitive Search.
Which three actions should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Your documents must include a field specifying which groups have access. This information becomes the filter criteria against which documents are selected or rejected from the result set returned to the issuer.
D: A query request targets the documents collection of a single index on a search service.
CF: In order to trim documents based on group_ids access, you should issue a search query with a group_ids/any(g:search.in(g, 'group_id1, group_id2,...')) filter, where 'group_id1, group_id2,...' are the groups to which the search request issuer belongs.
https://docs.microsoft.com/en-us/azure/search/search-security-trimming-for-azure-search
You need to build a chatbot that meets the following requirements:
Supports chit-chat, knowledge base, and multilingual models
Performs sentiment analysis on user messages
Selects the best language model automatically
What should you integrate into the chatbot?
Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.
QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your dat
a. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information.
Text Analytics: Mine insights in unstructured text using natural language processing (NLP)---no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input
Incorrect Answers:
A, B, D: Dispatch uses sample utterances for each of your bot's different tasks (LUIS, QnA Maker, or custom), and builds a model that can be used to properly route your user's request to the right task, even across multiple bots.
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/overview
You are building an app that uses a Language Understanding model to analyze text files. You need to ensure that the app can detect the following entities:
* Temperatures
* Currency values
* Email addresses
* Telephone numbers
The solution must minimize development effort.
Which model capability should you use?
You have an Azure Cognitive Search solution and an enrichment pipeline that performs Sentiment Analysis on social media posts.
You need to define a knowledge store that will include the social media posts and the Sentiment Analysis results.
Which two fields should you include in the definition? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You have an Azure subscription that contain an Azure OpenAI resource named AI1.
You build a chatbot that uses AI1 to provide generation answers to specific questions.
You need to ensure that the chatbot checks all input output for objectionable content.
Which types of resource should you create first?
Security & Privacy
Satisfied Customers
Committed Service
Money Back Guranteed