AI-901 Valid Test Labs & AI-901 Certification Training

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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 (Q61-Q66):

NEW QUESTION # 61
You are developing an application that processes voicemail recordings by using Azure Content Understanding in Foundry Tools. Which feature does Azure Content Understanding use to convert audio to text?

Answer: B

Explanation:
To convert audio to text for voicemail recordings in Azure Content Understanding, you should use the Transcription feature. This core capability leverages the Speech to Text service to automatically convert conversational audio into highly accurate, analyzable text.
Key Transcription Capabilities for Voicemails
Fast Transcription: Designed for applications that require predictable latency and rapid, near real- time conversion for shorter audio files like voicemails.
Text Formatting: Outputs text in structured, searchable formats (like WebVTT) with optional sentence- or word-level timestamps.
Multilingual Options: Automatically detects or locks to specific locales to accommodate diverse callers.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/audio/overview


NEW QUESTION # 62
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 63
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.

Which type of natural language processing was performed?

Answer: C

Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition , also known as Named Entity Recognition (NER) . NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.


NEW QUESTION # 64
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The completed sentence is:
Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability.
Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.
The other options are incorrect:
fairness focuses on avoiding bias and treating people fairly.
privacy and security focuses on protecting data and restricting access.
transparency focuses on explaining AI use, capabilities, and limitations.


NEW QUESTION # 65
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 # 66
......

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