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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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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Quiz Microsoft - Latest Exam AI-901 Quizzes

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Microsoft Azure AI Fundamentals Sample Questions (Q76-Q81):

NEW QUESTION # 76
Match the principles of responsible Al to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the light. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation:

AI systems must consistently operate as intended, even under unexpected conditions. = Reliability and safety This description matches the Microsoft responsible AI principle of reliability and safety, which focuses on ensuring that AI systems behave consistently, safely, and as expected, including under unusual or unexpected conditions.
AI systems must protect and secure personal and business information. = Privacy and security This description matches the Microsoft responsible AI principle of privacy and security, which focuses on protecting personal, confidential, and business-sensitive information and preventing unauthorized access or misuse.


NEW QUESTION # 77
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:

Explanation:

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze different content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format.
= Yes
Azure Content Understanding returns structured analysis output in JSON format.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can extract structured fields from documents and forms by using analyzers and schemas.


NEW QUESTION # 78
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?

Answer: D

Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
Why the other options are incorrect:
A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.
C . Compliance relates to governance and compliance, not model cost comparison.
D . Tools provides Foundry tools, not benchmarked cost comparison across models.


NEW QUESTION # 79
You have a Microsoft Foundry project that contains a deployed generative Al model You need to develop an application by using the Microsoft Foundry SDK to send chat prompts to the deployed model Which information must the application include?

Answer: C

Explanation:
To send chat prompts to a deployed model, the application needs authentication credentials, the endpoint URL, and the model deployment name . Microsoft's Foundry SDK guidance shows authentication with credentials, an endpoint, and the model= " model_deployment_name " value when calling a deployed model.


NEW QUESTION # 80
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?

Answer: B

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 # 81
......

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