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Microsoft AI-102 (Designing and Implementing a Microsoft Azure AI Solution) is a certification exam that is designed for professionals who want to demonstrate their skills in designing and implementing AI solutions using Microsoft Azure. Designing and Implementing a Microsoft Azure AI Solution certification exam is intended for AI engineers, data scientists, and developers who have experience in implementing AI solutions using Azure services. AI-102 Exam measures the candidate's ability to design and implement AI solutions that meet business requirements, and integrate with other Azure services.

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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q257-Q262):

NEW QUESTION # 257
You plan create an index for an Azure Cognitive Search service by using the Azure portal. The Cognitive Search service will connect to an Azure SQL database The Azure SQL database contains a table named UserMessages. Each row m User Messages has a field named MessageCopy that contains the text of social media messages sent by a user Users win perform full text searches against the MessageCopy field, and the values of the field will be shown to the users- You need to configure the properties of the index for the MessageCopy field to support the solution.
Winch attributes should you enable for the field?

Answer: B

Explanation:
* Searchable # Field content is tokenized and added to the full-text index (supports free-text queries).
* Retrievable # Field values can be included in search results.
* Sortable # Used for ordering results, not required here.
* Facetable # Used for aggregation/filtering by categories, not required here.
* Filterable # Used for structured filters (e.g., WHERE conditions), not required here.
Reference: Azure Cognitive Search field attributes
The answer: A


NEW QUESTION # 258
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.
After you answer a question in this section, you will NOT be able to return to it as a result, these questions will not appear in the review screen.
You are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You upload Doc1.pdf and train that contains a product catalogue and a price list.
During testing, users report that the chatbot responds correctly to the following question: What is the price of
< product > ?
The chatbot fails to respond to the following question: How much does < product > cost?
You need to ensure that the chatbot responds correctly to both questions.
Solution: from Language Studio, you create an entity for price, and then retrain and republish the model.
Does this meet the goal?

Answer: B

Explanation:
Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an "entity for price" would not make the chatbot correctly answer "How much does < product > cost?". The correct fix is to author additional alternate phrasings (as in Q266).
References
Custom question answering capabilities (no entity modeling).
https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview CLU entities vs. QnA authoring-separate features and workflows.
https://learn.microsoft.com/azure/ai-services/language-service/conversational-language-understanding
/overview


NEW QUESTION # 259
You have an app that analyzes images by using the Computer Vision API.
You need to configure the app to provide an output for users who are vision impaired. The solution must provide the output in complete sentences.
Which API call should you perform?

Answer: D

Explanation:
* To generate complete sentences describing an image (for accessibility for visually impaired users), you must call the Describe Image API.
* describeImageInStreamAsync analyzes the content of the image and generates human-readable descriptions in natural language sentences.
* readInStreamAsync is for OCR (extracting text).
* tagImageInStreamAsync is for generating tags (keywords), not full sentences.
* analyzeImageByDomainInStreamAsync is for domain-specific models (e.g., celebrities, landmarks).
Reference: Computer Vision Describe Images


NEW QUESTION # 260
You develop a Conversational Language Undemanding model by using Language Studio During testing, users receive incorrect responses to requests that do NOT relate to the capabilities of the model.
You need to ensure that the model identifies spurious requests.
What should you do?

Answer: D


NEW QUESTION # 261
What should you use to build a Microsoft Power Bi paginated report?

Answer: B


NEW QUESTION # 262
......

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