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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 2: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
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NEW QUESTION # 105
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
Answer: D
Explanation:
To monitor and control access to a custom agent in Azure, you must first create a Microsoft Foundry project. Once the project is created, you register your custom agent within it to enable management capabilities such as access control and activity monitoring.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/control-plane/register-custom-agent
NEW QUESTION # 106
You have a Microsoft Foundry project named Project1 that contains the following:
* An OpenAPI tool that calls an external API
* A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?
Answer: C
Explanation:
The correct action is to connect the OpenAPI tool to Connection1 . In Microsoft Foundry Agent Service, an OpenAPI tool does not automatically use every project connection in the project. For API key authentication, the tool must be explicitly configured to use the project connection that contains the required secret.
Microsoft's OpenAPI tool guidance states that API key or token authentication requires a project connection configured with the API key or token, and the tool is then created or configured to use that connection for authentication. The documentation also notes that the agent calls the external API by using the stored API key when the OpenAPI tool is configured with the project connection.
Option A is incorrect because a project's default connection is not automatically the authentication source for a specific OpenAPI tool. Option C is incorrect because identity passthrough or managed identity only applies when the target API accepts Microsoft Entra ID tokens; this scenario uses an external API key. Option D is also incorrect as a credential value should not be manually embedded in the OpenAPI specification. The specification defines the security scheme, while the secret value is stored in the Foundry connection.
Reference topics: OpenAPI tools, project connections, API key authentication, tool authentication configuration, and agent tracing.
NEW QUESTION # 107
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Authentication method: An Azure Login action that uses OpenID Connect (OIDC) If the evaluation results are NOT met, configure the workflow to: Fail The correct authentication method is Azure Login with OpenID Connect (OIDC) . Microsoft Foundry's GitHub Actions evaluation guidance recommends Microsoft Entra ID authentication and states that authentication can be automated by using the Azure Login GitHub action with OpenID Connect. The sample evaluation workflow also grants id-token: write, runs azure/login@v2, and then invokes the Microsoft AI Agent Evaluation action. This is the appropriate CI/CD authentication pattern because it avoids long-lived personal access tokens and supports secure federated authentication from GitHub Actions into Azure.
The workflow should be configured to fail when evaluation thresholds are not met. Foundry's evaluation GitHub Action is designed to automate pre-production assessment of Microsoft Foundry agents in CI/CD pipelines and produce evaluation results for the configured evaluators and test dataset. A failed GitHub Actions check can then be enforced through branch protection so the PR cannot merge until the quality gate passes. Locking the target branch or sending an alert does not directly implement a CI quality gate. Reference topics: Microsoft Foundry agent evaluation, GitHub Actions evaluation workflow, Microsoft Entra authentication, Azure Login with OIDC, pull-request quality gates, and CI/CD governance.
NEW QUESTION # 108
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:
Setting the output format parameter to Markdown is the correct and recommended action, but the exact property and enum name depend on whether you are interacting with the REST API directly or using the Python SDK.
To process PDF invoices and preserve their tables, headings, and visual sections in GitHub Flavored Markdown (GFM), configure your Azure Document Intelligence layout model parameters using the precise syntax detailed below.
Implementation Details
Python SDK Syntax: In the Azure Python client library, the parameter name is output_content_format, and its required value is DocumentContentFormat.MARKDOWN (rather than ContentFormat.MARKDOWN, which is used in the .NET C# SDK) Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/markdown-elements
NEW QUESTION # 109
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: A
Explanation:
The correct answer is B. Add a reflection pass before the responses are returned . A reflection pass is an application-orchestration step in which the generated summary is reviewed before final delivery, typically by asking the model or an evaluator step to check whether the answer covers the retrieved policy evidence and to revise the response when important details are missing. This directly addresses response completeness in application logic before the response is returned. The Microsoft Learn study guide explicitly includes Implement model reflection and Apply prompt engineering techniques to improve responses under optimization and operationalization of generative AI solutions.
This is also consistent with Microsoft Foundry agentic-loop guidance, which identifies reflection and planning cycles as patterns for multi-step reasoning in production agent systems. Completeness is a response-quality property: Azure AI evaluation defines completeness as whether a response contains all necessary and relevant information with respect to ground truth.
Option C is not correct because the scenario already says the agent generates summaries from retrieved policy documents, which is already a grounded retrieval pattern. Option A mainly reduces randomness, not missing content. Option D improves delivery experience, not answer completeness. Reference topics: model reflection, prompt engineering, agentic loops, response evaluation, and grounded generative AI solutions.
NEW QUESTION # 110
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