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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Related Certifications: | Microsoft Azure Data Fundamentals (DP-900) Microsoft Azure Fundamentals (AZ-900) |
| Exam Duration: | 60 minutes |
| Exam Price: | Approx. 99 USD (varies by region) |
| Passing Score: | 700 (out of 1000) |
| Available Languages: | Spanish, Japanese, Korean, French, German, English, Chinese (Simplified), Portuguese (Brazil), Chinese (Traditional) |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Real Exam Qty: | 40-60 |
| Exam Format: | Multiple choice, Case study (limited), Multiple response, Drag and drop |
| Recommended Training: | Microsoft Learn - AI-900 Learning Path Azure AI Fundamentals Course |
| 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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질문 # 76
Hotspot Question
You are developing a voice application that listens for spoken commands and converts them into text 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.
정답：
설명：
질문 # 77
Select the answer that correctly completes the sentence.
정답：
설명：
Explanation:
The correct selection is AIProjectClient. In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project.
Microsoft documentation specifically exposes operation groups through this client for deployments, agents, and indexes, which directly matches the requirements stated in the question.
For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.
ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.
Therefore, the class required for this scenario is AIProjectClient.
질문 # 78
Hotspot Question
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
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.
정답：
설명：
Explanation:
Box: analyzer_id
Core Method Signature
poller = client.begin_analyze(
analyzer_id="YOUR_ANALYZER_ID",
inputs=[AnalysisInput(url="FILE_URL")],
**kwargs
)
Note:
client.begin_analyze is the primary method used in the official Azure AI Content Understanding Python SDK (azure-ai-contentunderstanding) to kick off asynchronous, long-running operations (LRO) for extracting structured data and layout information from multimodal files.
Formerly a part of standalone Azure AI services, Content Understanding is fully integrated into the Microsoft Foundry ecosystem. This service utilizes foundational large language models deployed inside your Foundry resource to analyze documents, images, audio, and video streams based on prebuilt or custom schemas.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api
질문 # 79
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
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.
정답：
설명：
Explanation:
The correct parameter is analyzer_id. The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer, that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]), confirming the required SDK pattern
질문 # 80
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.
정답：
설명：
Explanation:
Box 1: No
No, you do not need to manually construct HTTP requests when using the Microsoft Foundry SDK. The SDK provides high-level client abstractions, such as AIProjectClient, that allow you to interact with your deployed models using standard programming languages and object-oriented paradigms.
Box 2: No
No, you do not need to create a separate web service to interact with a deployed model when using the Microsoft Foundry SDK.
The Microsoft Foundry SDK allows your application to communicate directly with the cloud-hosted model endpoint through built-in project clients.
Box 3: Yes
Yes, the AIProjectClient class in the Microsoft Foundry SDK (part of the Azure AI Projects client library) provides programmatic access to manage and interact with a deployed project and its connected resources.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/endpoints
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/sdk-overview
https://learn.microsoft.com/en-us/dotnet/api/overview/azure/ai.projects-readme
질문 # 81
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