AI-901 Latest Exam Labs & Practice AI-901 Test Engine

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Microsoft AI-901 Exam Syllabus Topics:

TopicDetails
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.
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 Azure AI Fundamentals Sample Questions (Q31-Q36):

NEW QUESTION # 31
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: C

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()


NEW QUESTION # 32
You need to identify harmful content in a generative AI solution that uses Azure OpenAI Service.
What should you use?

Answer: C

Explanation:
Azure AI Content Safety is a new Azure AI Service that can detect harmful user-generated and AI-generated content. This service is being integrated across MS products, including Azure OpenAI Service and Machine Learning prompt flow.
Reference:
https://ivanatilca.medium.com/testing-azure-ai-content-safety-ccd512f9b350


NEW QUESTION # 33
What is an example of a Microsoft responsible AI principle?

Answer: A

Explanation:
Correct:
* AI systems should be secure and respect privacy.
* AI systems should treat people fairly.
Incorrect:
* AI systems should be in the public domain.
* AI systems should make personal details accessible.
* AI systems should NOT reveal the details of their design.
* AI systems should protect the interests of developers.
* AI systems should use black-box models.
Note:
Responsible AI principles
*-> Fairness: AI systems should treat all people fairly.
AI systems should treat everyone equally and provide the same recommendations to all individuals. Fairness in AI systems prevents discrimination based on personal characteristics.
* Reliability and safety: AI systems should perform reliably and safely.
*-> Privacy and security: AI systems should be secure and respect privacy.
* Inclusiveness: AI systems should empower everyone and engage people.
* Etc.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai


NEW QUESTION # 34
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations.
Which Azure speech class should you use to configure the connection to the Azure Speech service?

Answer: A


NEW QUESTION # 35
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 # 36
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