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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Topic 2: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 21
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
Answer: A
Explanation:
The correct answer is D. Set the output_content_format=ContentFormat.MARKDOWN value . Azure Document Intelligence Layout API can return extracted document content in Markdown format, preserving semantic structure such as headings, paragraphs, sections, tables, and other layout elements. Microsoft's Document Intelligence layout guidance shows the Python SDK pattern for analyzing a document with the prebuilt-layout model and setting output_content_format=ContentFormat.MARKDOWN in the begin_analyze_document call. The Markdown output is returned in the top-level content section of the analysis result.
This minimizes development effort because the service produces structure-preserving Markdown directly, rather than requiring custom post-processing to reconstruct sections and table formatting from raw OCR spans. Microsoft's Markdown output documentation states that specifying Markdown output produces semantically structured content that maintains paragraphs, headings, tables, and other document elements in their proper hierarchy.
Option A only changes validation behavior and does not generate Markdown. Option B requests figures, not structured Markdown. Option C uses an incorrect parameter name; the documented SDK setting is output_content_format, not content. Reference topics: Azure Document Intelligence Layout API, Markdown output, PDF analysis, table extraction, and Foundry Tools document processing.
NEW QUESTION # 22
You have an Azure subscription that contains an Azure Language in Foundry Tools service resource. You need to identify the URL of the REST interface for the Language service. Which blade should you use in the Azure portal?
Answer: D
Explanation:
The Keys and Endpoint blade displays the resource-specific endpoint URL used to construct REST API requests to Azure Language in Foundry Tools. It also provides the resource access keys when local key-based authentication is enabled. Microsoft's Azure Language documentation directs administrators to open the resource in the Azure portal and select Keys and Endpoint from the left menu to obtain the endpoint and credentials required for API calls.
The endpoint normally forms the base URI for the service. The application appends the operation-specific route and API version required by features such as sentiment analysis, named entity recognition, entity linking, or conversational language understanding. The key may then be supplied in the request's authentication header, although Microsoft Entra ID and managed identities are recommended for supported production workloads because they avoid embedding secrets in application code.
The Networking blade controls public access, firewalls, virtual networks, and private endpoints. Identity manages the resource's managed identity. Properties presents general resource metadata but is not the designated location for retrieving the REST API endpoint and access keys.
Study Guide alignment: plan and manage Azure AI resources, configure service access, and integrate Foundry Tools through their APIs and authentication mechanisms .
NEW QUESTION # 23
You have an Azure AI Search indexer that ingest PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
Answer: D
Explanation:
To meet the requirements, you need to use the Azure Content Understanding skill in your Azure AI Search skillset.
Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-skill-content-understanding
NEW QUESTION # 24
Your company is piloting a customer support agent in a Microsoft Foundry project name Project1. Project1 is connected to an existing Application Insights resource, and the company ' s support team reviews runs in the Traces tab.
The Foundry Agent Service is configured to perform the following actions:
* Retrieve the Application Insights connection string by calling
project_client.telemetry.get_application_insights_connection_string().
* Call configure_azure_monitor(connection_string=...) to enable telemetry.
A separate LangChain service configured to use OpenTelemetry and has the following configurations:
* Uses AzureAIOpenTelemetryTracer(connection_string=..., enable_content_recording=False)
* Passes the tracer by using config={ " callbacks " :[azure_tracer]}
Company policy has the following requirements:
* Telemetry from LangChain and OpenTelemetry must be distinguishable within the same Application Insights resource.
* Secrets and credentials must NOT be stored in prompts, tool arguments, or span attributes.
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:
The LangChain service will appear in Traces without configuring a tracer: No Setting different OTEL_SERVICE_NAME values separates the services in Application Insights: Yes When using enable_content_recording=False, prompts and tool data will be captured in the telemetry: No The first statement is No because a separate LangChain or LangGraph application must emit telemetry through the configured tracing integration. Microsoft's LangChain tracing guidance states that you configure AzureAIOpenTelemetryTracer, attach it to the runnable or agent through callbacks, and then inspect the emitted traces in Azure Monitor. The troubleshooting guidance also states that missing LangChain or LangGraph spans are caused by tracing callbacks not being attached to the run.
The second statement is Yes . In OpenTelemetry, OTEL_SERVICE_NAME maps to the service.name resource attribute. Azure Monitor Application Insights uses cloud role names to represent separate services, and Microsoft states that when multiple services emit to the same Application Insights resource, cloud role names must be set so services are represented properly.
The third statement is No . enable_content_recording=False is specifically used to redact message content and tool call arguments from traces. Microsoft also advises disabling content recording in production and not storing secrets, credentials, or tokens in prompts or tool arguments. Reference topics: Microsoft Foundry tracing, LangChain tracing, OpenTelemetry service naming, Application Insights, and secure telemetry configuration.
NEW QUESTION # 25
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 26
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