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Microsoft AI-901 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Microsoft Azure AI Fundamentals (AI-900) Exam
Exam Number:AI-900
Passing Score:700 (out of 1000)
Available Languages:Spanish, Chinese (Simplified), Japanese, English, Korean, German, French, Chinese (Traditional), Portuguese (Brazil)
Related Certifications:Microsoft Azure Fundamentals (AZ-900)
Microsoft Azure Data Fundamentals (DP-900)
Exam Format:Multiple response, Multiple choice, Case study (limited), Drag and drop
Exam Price:Approx. 99 USD (varies by region)
Exam Duration:60 minutes
Real Exam Qty:40-60
Certificate Validity Period:Does not expire (Fundamentals certification)
Recommended Training:Azure AI Fundamentals Course
Microsoft Learn - AI-900 Learning Path
Exam Registration:Microsoft Certification Portal
Schedule exam via Pearson VUE
Sample Questions:Microsoft AI-901 Sample Questions
Exam Way:Online proctored exam or in-person test center
Pre Condition:No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/

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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.

Microsoft Azure AI Fundamentals Sample Questions (Q81-Q86):

NEW QUESTION # 81
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: A summarization model is used to transcribe spoken audio into written text. = No Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.
Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models , not chat completion models.
Statement 3: An embedding model can generate vector representations of the provided input. = Yes This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.


NEW QUESTION # 82
Hotspot Question
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 83
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: B


NEW QUESTION # 84
Hotspot Question
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:
Box 1: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can perform optical character recognition (OCR). The built-in Read OCR engine extracts both printed and handwritten text in multiple languages and writing styles from images and documents.
Box 2: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can automatically generate concise one-sentence human-readable captions and descriptive content tags for thousands of recognizable objects, living beings, actions, and settings.
Box 3: No
Image analysis capabilities in Foundry Tools are designed to process, interpret, and extract insights from existing images, rather than create new ones.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-image-analysis


NEW QUESTION # 85
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 # 86
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