Microsoft AI-901 Valid Vce Dumps | AI-901 Free Practice Exams

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| Topic | Details |
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| Topic 1 | - Identify AI concepts and capabilities: This domain covers the foundational knowledge of AI from ethical principles and responsible design to understanding how AI models work and what kinds of tasks they can perform. It also explores the full range of AI workloads including generative AI, computer vision, speech, and information extraction.
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| Topic 2 | - Implement AI solutions by using Microsoft Foundry: This domain is hands-on and focuses on building and deploying AI solutions using the Microsoft Foundry platform and its associated tools. It spans generative AI apps, text and speech processing, computer vision, and document intelligence all implemented through the Foundry portal and SDK.
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>> Microsoft AI-901 Valid Vce Dumps <<
AI-901 Free Practice Exams | AI-901 Online Training Materials
Work hard and practice with our Microsoft AI-901 dumps till you are confident to pass the Microsoft AI-901 exam. And that too with flying colors and achieving the Microsoft AI-901 Certification on the first attempt. You will identify both your strengths and shortcomings when you utilize Microsoft Azure AI Fundamentals practice exam software.
Microsoft Azure AI Fundamentals Sample Questions (Q36-Q41):
NEW QUESTION # 36
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 37
You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?
- A. Use optical character recognition (OCR) to extract text from the PDF and map the fields in the code.
- B. Call analyze() to return the extracted fields synchronously in the same request.
- C. Submit the PDF to an analyzer and read the results from the request headers.
- D. Call begin_analyze(), and then call poller.result() to retrieve the results.
Answer: D
Explanation:
Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().
Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:
result = poller.result()
Option B is incorrect because extraction results are not read from request headers.
Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.
Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.
NEW QUESTION # 38
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 39
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 40
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:
Explanation:

Explanation:
Box: the Azure Language MCP server
To enable an AI agent in Microsoft Foundry to utilize Azure Language capabilities for text analysis, you must configure the Azure Language Model Context Protocol (MCP) server.
Microsoft Foundry integrates its ecosystem of deterministic AI services through the standardized Model Context Protocol (MCP). This removes the need to write custom routing logic or stitch together multiple standalone APIs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview
NEW QUESTION # 41
......
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