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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 74
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,B
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 # 75
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:
Explanation:
temperature = 0
output_config = { " effort " : " high " }
The correct configuration is temperature = 0 and output_config = { " effort " : " high " }. The requirement is to reduce small wording variations that are breaking automated validation. In chat completion requests, temperature controls sampling randomness. Microsoft's Azure OpenAI reference states that temperature ranges between 0 and 2, and that higher values make output more random while lower values make output more focused and deterministic. Therefore, the most stable setting from the available choices is 0, because it minimizes randomness and improves repeatability for validation-sensitive response patterns.
The solution must also maximize reasoning quality. The code already enables thinking with thinking={ " type
" : " enabled " }, so the remaining reasoning-quality control is the effort setting. Microsoft Foundry model guidance states that the effort parameter controls the quality/cost tradeoff and supports low, medium, and high effort levels. Selecting " high " maximizes reasoning quality among the available options.
Using temperature values of 1 or 2 would increase variability and make validation mismatches more likely.
Selecting low or medium effort would not meet the requirement to maximize reasoning quality. Reference topics: Microsoft Foundry model inference, chat model parameters, temperature, thinking, effort, and output stability.
NEW QUESTION # 76
You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service.
The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1.
You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked.
What should you configure in the OpenAPI specification?
Answer: B
Explanation:
The correct configuration is an API key security scheme . For Microsoft Foundry Agent Service OpenAPI tools, the OpenAPI specification must declare authentication through the components.securitySchemes section and use a scheme of type apiKey when the external service expects a key in a header. Microsoft's OpenAPI tool guidance states that API key authentication requires updating the OpenAPI spec security schemes with one scheme of type apiKey, and the tool then uses the associated project connection to supply the key value at runtime. This allows the key stored in Project1's connection to be injected automatically when the tool is invoked.
A header parameter defined separately for each operation is not the correct approach because credentials should not be modeled as ordinary operation parameters. The Foundry guidance explicitly indicates that parameters requiring the API key should be removed from the OpenAPI spec because the API key is stored and passed through a connection. A Bearer token security scheme is used for bearer-token-style authorization, not a generic weather API key passed in a custom HTTP header. Azure Key Vault is a secret store, but the scenario already stores the key in a Foundry project connection. Reference topics: Microsoft Foundry Agent Service, OpenAPI tools, project connections, API key authentication, and OpenAPI security schemes.
NEW QUESTION # 77
You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?
Answer: A
Explanation:
One should absolutely use a model cascade to route requests to different models based on complexity. This architectural pattern is highly effective for high-volume chat applications because it directly addresses the trade-off between operational cost, API latency, and response quality.
Using a model cascade router ensures that your high-volume Microsoft Foundry application scales efficiently by reserving expensive computational power exclusively for queries that actually require advanced cognitive processing.
Note:
1. Analyze Request Complexity
Implement a lightweight intent classifier or routing layer at the entry point of your Microsoft Foundry project. This router quickly inspects incoming user prompts using basic heuristic keyword matching, semantic embeddings, or a highly optimized, fast model (like Phi-3 or GPT-
4o-mini) to categorize the query as either a "Simple FAQ" or a "Complex Reasoning" request.
2. Route to the Optimal TierTier 1 (Fast & Cheap):
Route standard, predictable FAQ requests to a smaller, cost-effective model or a local cache/vector database lookup. This keeps latency in milliseconds and drastically lowers token costs.
Tier 2 (Advanced Reasoning): Route multi-step logic, coding, or highly contextual queries to a frontier model (like GPT-4o).
3. Implement Fallback LogicDesign the cascade to be dynamic. If the smaller Tier 1 model generates a response with low confidence, or if the user asks a follow-up question that invalidates the simple FAQ status, seamlessly upgrade the conversation loop to the Tier 2 model.
Reference:
https://medium.com/@sujathamudadla1213/what-is-the-primary-purpose-of-a-model-cascade-in-machine-learning-0b145a7bc6e2
NEW QUESTION # 78
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?
Answer: A
Explanation:
To best manage security and centrally handle credentials across multiple agents, you should add a connection to the Azure AI Search resource at the Azure AI Foundry project level.
Why This Works
Central Hub: The project acts as the single security perimeter for all your agents.
Credential Masking: Agents inherit access without hardcoding secrets, API keys, or connection strings in their code.
Identity Management: It allows you to leverage Microsoft Entra ID (formerly Azure AD) for role- based access control (RBAC).
How to Implement It
1. Navigate to your Azure AI Foundry portal.
2. Select your specific project from the dashboard.
3. Open the "Management Center" or "Project settings" tab.
4. Click on "Connected resources" or "Connections".
5. Add the Azure AI Search resource.
6. Choose Entra ID (managed identity) over API keys for maximum security.
Reference:
https://partner.microsoft.com/en-us/blog/article/azure-updates-december-2025
NEW QUESTION # 79
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