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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
Die Microsoft AI-103 Zertifizierungsprüfung ist der erste Schritt zum Berufserfolg fur IT-Fachleute. Durch die Microsoft AI-103 Zertifizierungsprüfung haben Sie schon den ersten Fuß auf die Spitze Ihrer Karriere gesetzt. DeutschPrüfung wird Ihnen helfen, die Microsoft AI-103 Zertifizierungsprüfung zu bestehen.
147. Frage
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a dYouloyed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
148. Frage
A finance team must extract named fields, such as invoice number and total, from invoices, and they need a per-field confidence score so that low-confidence values can be routed to a human reviewer. Which Azure Content Understanding mode should they use?
Antwort: B
Begründung:
Standard mode in Azure Content Understanding supports extract fields and returns confidence scores and grounding, which is exactly what a human-in-the-loop review workflow needs. Pro mode is built for multi-step reasoning across documents and deliberately omits confidence scoring.
Content Understanding pro mode currently doesn't offer confidence scores or grounding. It supports classify and generate fields, but it doesn't support extract fields.
149. Frage
You have a Microsoft Foundry project that contains an agent. The agent uses two tools to perform the following actions:
* Use Azure AI Search to retrieve answers from a private product documentation index.
* Use the web search tool to retrieve public information on the internet.
You need to ensure that for a specific run, the agent deterministically retrieves information only from the internet. To what should you set tool_choice?
Antwort: C
Begründung:
Set tool_choice to the named Grounding with Bing Search tool:
{ " type " : " bing_grounding " }
A named tool choice directs the model to invoke a specific tool rather than allowing it to select from all tools attached to the agent. Microsoft's SDK specification describes AgentsNamedToolChoice as the mechanism used to force the model to call a particular tool and lists bing_grounding and azure_ai_search as supported named tool types.
Grounding with Bing Search retrieves current public information from the internet and supplies the results to the model for response generation. Selecting bing_grounding therefore directs the run to the public web source rather than the private Azure AI Search index.
Setting tool_choice to " required " only establishes that a tool must be invoked; when multiple tools are available, it does not identify which one must be selected. " auto " delegates the decision entirely to the model. Selecting { " type " : " azure_ai_search " } would force retrieval from the private product- documentation index, which is the opposite of the requirement.
Study Guide alignment: configure agent tools, control tool selection, integrate web grounding, and connect agents to enterprise knowledge sources .
150. Frage
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page- number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
Antwort: B
151. Frage
You have a Microsoft Foundry project that contains an agent.
You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
scope = " {{userId}} "
tools = [memory_tool]
The correct scope value is {{userId}} because the requirement is per-authenticated-user memory isolation without the client application manually generating user identifiers. In Microsoft Foundry Agent Service memory, the scope parameter partitions memory items inside the memory store. The official guidance states that when the memory search tool is attached to an agent, setting scope to the user identity template enables per-user memory isolation; the service resolves the end-user identity from the request header when provided, or falls back to the Microsoft Entra tenant ID and object ID of the caller. This matches the requirement to isolate stored preferences by authenticated user automatically.
The tools property must be [memory_tool] because the MemorySearchTool instance is created earlier and must be attached to the PromptAgentDefinition. Foundry guidance shows the memory search tool being passed in the agent definition as tools=[tool] , allowing the agent to read from and write to the configured memory store during conversations.
" session " and {{conversationId}} would limit continuity to a session or conversation instead of enabling long-term recall across separate conversations. [mem_store_name] is a list containing the store name, not a tool definition. Reference topics: Foundry Agent Service memory, memory stores, memory search tools, scope, and per-user isolation.
152. Frage
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