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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Exam Duration: | 45-60 |
| Available Languages: | Italian, Arabic (Saudi Arabia), Portuguese (Brazil), Japanese, Russian, Spanish, Chinese (Traditional), French, Chinese (Simplified), Korean, Indonesian (Indonesia), English, German |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Passing Score: | 700 / 1000 |
| Exam Format: | Drag and drop, Multiple choice, Scenario-based, Multiple select, Match the service to use case |
| Real Exam Qty: | 40-60 |
| Exam Price: | USD 99 |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
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質問 # 28
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?
正解：C
解説：
The described solution coordinates a multi-step workflow: gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI.
質問 # 29
You are developing an application named App1 that analyzes customer feedback.
App1 will call Azure Language in Foundry Tools from a generative AI agent in Microsoft Foundry.
You need to ensure that the agent can access Azure Language text analysis.
What should you configure?
正解：A
解説：
The Azure Language MCP (Model Context Protocol) server needs to be configured so your generative AI agent can utilize text analysis tools (like sentiment analysis, keyphrase extraction, and language detection).Azure Language in Foundry Tools exposes its capabilities as standardized tools using the MCP framework. This means your agent cannot call the traditional REST API directly; it must route requests through an MCP server configuration.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview
質問 # 30
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.
正解：
解説：
質問 # 31
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services. = No Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to- speech synthesis features.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This is correct. Microsoft's guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.
質問 # 32
Your company has thousands of recorded customer support calls in multiple languages stored as audio files in Azure Storage.
You need to generate text transcripts of all the recordings.
Which Azure Speech in Foundry Tools capability should you use?
正解：C
解説：
For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription .
Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage , including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.
Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio.
Speech translation translates speech between languages, but the requirement is to generate transcripts.
質問 # 33
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