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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 AI-901 Exam Braindumps | AI-901 Dumps Questions

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Microsoft Azure AI Fundamentals Sample Questions (Q12-Q17):

NEW QUESTION # 12
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous .
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
* Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
* Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
* Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.


NEW QUESTION # 13
You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users. What should you configure?

Answer: D

Explanation:
In Microsoft Foundry, instructions (formerly referred to as system messages) are the primary way to define the role, behavior, tone, and constraints of your AI agent.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/agents/quickstart


NEW QUESTION # 14
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: An AI generative model is retrained before performing each user request. = No A generative AI model is not retrained for every user request. During normal use, the model performs inference: it receives input, processes the prompt, and generates a response based on the deployed model.
Statement 2: An AI agent responds by copying and pasting answers stored in a database. = No An AI agent does not simply copy and paste stored answers. Agents use generative AI models, instructions, context, and optionally tools or connected data sources to reason over user input and produce responses or actions.
Statement 3: An AI agent uses a generative AI model to establish actions based on user input. = Yes This is correct. An AI agent uses a generative AI model together with instructions and available tools to interpret user input, determine the next action, and generate a response.


NEW QUESTION # 15
Hotspot Question
Select the answer that correctly completes the sentence.

Answer:

Explanation:


NEW QUESTION # 16
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 # 17
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