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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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Microsoft Azure AI Fundamentals Sample Questions (Q117-Q122):

NEW QUESTION # 117
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 # 118
What is the maximum image size that can be processed by using the prebuilt receipt model in Azure AI Document Intelligence?

Answer: D

Explanation:
Document Intelligence receipt model
Supported file size: less than 50 MB; minimum pixels 50 x 50 px; maximum pixels 10,000 x
10,000 px
For custom extraction model training, the total size of training data is 50 MB for template model and 1 GB for the neural model.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/id-document


NEW QUESTION # 119
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: C

Explanation:
To create an AI agent that follows a specific role and behavior, you configure system instructions.
Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior.


NEW QUESTION # 120
Drag and Drop Question
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right.
Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Transcribes spoken input
Speech recognition in Microsoft Foundry transcribes spoken input into text using built-in speech- to-text capabilities.
Transcription FeaturesReal-time transcription: Converts microphone inputs or live audio streams into text instantly.
Batch transcription: Processes pre-recorded audio or video files asynchronously.
Advanced audio models: Leverages tools like GPT-transcribe and GPT-live-transcribe to handle natural speech patterns, numbers, addresses, and industry-specific terminology.
Broad language support: Handles over 140 locales and languages for spoken input.
Box 2: Generates spoken output
Speech synthesis in Microsoft Foundry (via Azure Speech in Foundry Tools) converts text input into realistic, human-like spoken audio output.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text


NEW QUESTION # 121
Capturing text from images is an example of which type of AI capability?

Answer: C

Explanation:
Capturing text from images is the exact definition of optical character recognition (OCR).
How OCR Works
Image input: Software takes a photo, scan, or graphic containing letters.
Shape analysis: It scans the pixels to find light and dark areas that match letters or numbers.
Text output: It changes the visual shapes into real, editable digital text.
Reference:
https://en.wikipedia.org/wiki/Optical_character_recognition


NEW QUESTION # 122
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