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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 17
Hotspot Question
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
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.
Answer:
Explanation:
NEW QUESTION # 18
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 # 19
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
NEW QUESTION # 20
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
Answer: B
Explanation:
To enable the agent to answer questions about order status, you need a plugin.
In Semantic Kernel, a plugin acts as a container for the specific capabilities (functions) that allow an agent to interact with external data or systems, such as an order database or API.
To help me give you a more specific example or guide, could you tell me:
The data source where order info is kept (e.g., a SQL database, a REST API, or Shopify) The programming language you're using for the Kernel (C# or Python) Reference:
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions
NEW QUESTION # 21
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
* Ensure that background objects can be removed by applying a mask-based inpainting edit.
* Preserve the original lighting and style of the edited images.
* Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: A
Explanation:
The correct configuration is D. Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify . The requirement is not to generate a new image, but to edit a specific region of an existing image while preserving the surrounding lighting, composition, and style. Azure OpenAI image editing in Microsoft Foundry supports modifying existing images by submitting an input image plus a prompt. For masked edits, the mask explicitly defines the part of the image the model is allowed to change; Microsoft states that the mask parameter defines the area to edit and must match the input image dimensions.
text_to_image would create a new image from a prompt and cannot guarantee preservation of the original image. image_variation generates related variants rather than targeted removals. image_to_image with high strength can regenerate broader areas and may alter unrelated visual details. Mask-based inpainting is the built-in editing control that limits modification to the selected region. Reference topics: Azure OpenAI image editing, mask inpainting, image edit API, input image, mask parameter, and computer vision image generation workflows.
NEW QUESTION # 22
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