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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 3: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 5: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
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NEW QUESTION # 37
You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?
Answer: A
Explanation:
To configure end-to-end visibility into each step of the request workflow, you should Enable application tracing in the project.
Project-Level Observability: Enabling application tracing allows Microsoft Foundry to natively track, trace, and monitor every component of the workflow-including calls to the Azure OpenAI Responses API and associated tools like Azure AI Content Safety-within the same request without requiring complex, separate SDK configurations.
Centralized Tracking: Tracing captures the exact execution path across the project endpoints, allowing you to see metrics, latency, and inputs/outputs sequentially for both the model invocation and the content safety check.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/trace-production-sdk
NEW QUESTION # 38
You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?
Answer: A
Explanation:
The most appropriate solution is Azure Content Understanding in Foundry Tools.
The Azure Content Understanding service natively combines advanced Optical Character Recognition (OCR), deep layout analysis, and pre-built generative capabilities. It handles varied document structures, multi-page tables, and template-generalizing field extraction without requiring custom machine learning model training. It operates as a low-administration, out-of-the- box solution perfectly aligned with document intelligence needs.
Incorrect:
[Not A]
Azure Language in Foundry Tools: Azure AI Language focuses primarily on unstructured text analytics, sentiment analysis, text summarization, and conversational capabilities. It lacks the built-in document layout analysis, table parsing, and visual OCR capabilities necessary to process complex scanned PDF invoice structures.
[Not C]
Azure Machine Learning model: Building, training, deploying, and managing a custom model in Azure Machine Learning requires significant data science expertise. This approach introduces high administrative overhead, complex infrastructure management, and manual pipeline maintenance, which violates the requirement for low administration.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5706482/azure-document-intelligence-and-content-understand
NEW QUESTION # 39
You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
Answer: C
Explanation:
To deploy the Azure OpenAI DALL-E model, the most appropriate tools to use are Azure AI Studio for model management and interaction, and the Azure Command-Line Interface (CLI) for deployment and configuration tasks. Azure AI Studio provides an interactive interface for working with Azure AI models, and the Azure CLI allows you to manage resources, deployments, and other Azure services from the command line.
NEW QUESTION # 40
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
Answer: B
Explanation:
The correct answer is D. the custom speech project ID . For custom speech fine-tuning, the Speech to text REST API uses a project property that must refer to the Custom Speech project, not the general Microsoft Foundry project. Microsoft's Custom Speech guidance states that when using the Speech to text REST API for custom speech, you must set the project property to the ID of your custom speech project. It also explicitly notes that the custom speech project ID is not the same as the Microsoft Foundry project ID.
This distinction explains the invalid project ID error. Supplying the Foundry project ID, project URL, or endpoint URL does not identify the Custom Speech project that owns the fine-tuned speech model. The custom speech endpoint URL is used when calling a deployed custom model endpoint for recognition, but it is not the value of the REST API project property. The project URL is also not accepted because the API expects the identifier value. Reference topics: Azure Speech in Foundry Tools, Custom Speech fine-tuning, Speech to text REST API, custom speech project ID, model publication, and endpoint configuration.
NEW QUESTION # 41
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
* Each workflow run must include a retrieval step before generating a response.
* Tool calls must authenticate by using the published agent's own identity.
* Tool access must use an identity isolated from other project resources.
* Tool access must support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Set tool_choice to: required
Configure the tool to authenticate by: Using a distinct agent identity bound to the client application Set tool_choice to required because the compliance workflow must deterministically include a tool-based retrieval step before the agent generates a response. Microsoft Foundry Agent Service guidance states that tool_choice provides the most deterministic control over tool use: auto lets the model decide, none prevents tool calls, and required forces the model to call one or more tools. This directly corrects the current nondeterministic behavior where the model decides whether to call tools.
For authentication, use a distinct agent identity bound to the client application . Microsoft Foundry creates a shared identity for unpublished or in-development agents, but publishing an agent automatically creates a dedicated agent identity blueprint and agent identity associated with the agent application resource. Published agents authenticate tool calls by using that unique agent identity, and RBAC permissions must be assigned to the new identity. This provides isolation from the broader shared project identity and supports independent audit trails for compliance workflows.
Storing API keys in prompts violates security guidance and prevents robust audit attribution. The shared project agent identity is easier for development, but it has a broader blast radius and does not meet the isolation requirement. Reference topics: Foundry Agent Service tool choice, tool authentication, published agent identities, RBAC, and auditability.
NEW QUESTION # 42
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