Microsoft Developing AI Apps and Agents on Azure (AI-103)

You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
 

You need to configure Agent1 to meet the security and compliance requirements.

What should you use?

You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure AI Search resource.

You need to recommend a solution to centrally manage the Azure AI Search credentials within Project1. The solution must be implemented across all the agents.

What should you recommend?

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 is 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.

You need to configure personalized user interactions for Agent1. The solution must meet the business requirements.

What should you include in the solution?

You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.
You need to implement two pipelines by using Azure Content Understanding in Foundry Tools. The solution must meet the following requirements:
Include a pipeline named Pipeline1 that supports cost-effective, high-volume processing of standalone PDF invoices.
Include a pipeline named Pipeline2 that supports cross-document validation by using multi-step reasoning and reference data.
How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration may be used once, more than once, of not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
 

You need to ensure that Agent1Dev Team can access Agent1. The solution must meet the security and compliance requirements.

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.

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 deployed 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.

You have a Microsoft Foundry project that contains a Retrieval Augmented Generation (RAG) chat solution used by customer support agents.

You are adding an automated pre-production evaluation step to a CI/CD pipeline named Pipeline1. The evaluation will run against a labeled test dataset that contains support questions and the expected grounding context.

You need to ensure that Pipeline1 fails if unsupported content or a retrieval mismatch exceeds a defined threshold:

• responses include claims not supported by the retrieved source content

• retrieved source content does not align with the labeled expected context

Which two built-in evaluators should you use in Pipeline1? Each correct answer presents pat of the solution.

NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
An if/else condition expression that ensures that Var01 contains a value
A Send message expression that returns the stored user response in uppercase
How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project that contains a support-ticket triage agent built by using the Foundry Agent Service.

The agent uses tool to classify the ticket type and sot the ticket priority.

Sometimes, the same support case continues across multiple sessions over several days.

You need to persist state by using a durable ID to ensure that the agent can automatically reuse the full interaction history. The solution must preserve previous user messages, tool calls and tool outputs across turns and sessions.

Which runtime component should you use?

You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure AI Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.

You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:

• Whether responses address the user query

• Whether responses are supported by the provided context

• Whether responses contain sensitive or proprietary information

Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project that contains three agents as shown in the following table.

You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.

You need to provide scores for groundedness, relevance, and harmful content categories.

Which two evaluation categories can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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?

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?

You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.

The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.

You need to reduce response variability, without modifying the prompt or reducing factual accuracy.

What should you do for the model?

You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.

You are approaching the expiration date of the custom speech to text model.

What is the expected behavior when the model expires?

You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.

You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.

Which prompt engineering approach should you use?

You have a Microsoft Foundry project that contains a model deployment.

You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.

The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.

You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.

Which role-based access control (RBAC) role should you assign to the developers?

You have a Microsoft Foundry project that contains an agent for a customer support chat app. The agent uses a memory store and a memory search tool.

You need to ensure that the conversation history does NOT persist across separate sessions.

To what should you set the scope of the memory tool?

You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search.
Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations.
You are provided with the following code snippet that runs the agent.