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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design AI-powered business solutions | 30% | - Extensibility and orchestration
|
| Topic 2: Plan AI-powered business solutions | 25% | - AI solution design strategy
|
| Topic 3: Deploy AI-powered business solutions | 45% | - Testing and validation
|
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NEW QUESTION # 27
What should you include in the custom Al agent design to meet the R & D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer : =
* To expose the data to the agent, create: a custom connector
* Add to the agent: the MCP server
The custom agent must answer questions about product specifications and compliance information , and the case study states that the R & D department already has a custom Model Context Protocol (MCP) server containing that information.
The best design is:
* create a custom connector to expose that external capability cleanly to the low-code Copilot solution
* add the MCP server to the agent so the agent can use that external knowledge/tooling source Why this is correct:
* The MCP server already exists and contains the needed product-specification and compliance data.
* In a Copilot/agent design, you need a way to expose external functionality and data in a reusable, secure way. A custom connector is the low-code integration mechanism that fits this requirement.
* Then the agent can use the MCP server as the connected external capability for answering those questions.
Why the other options are not correct:
* Azure AI Bot Service channel is for communication channels, not for exposing this knowledge source.
* a custom OData entity is not the right pattern for integrating the existing MCP-based capability.
* the Semantic Kernel is a developer orchestration framework, but the requirement emphasizes using the existing MCP technology in a low-code solution.
* an event trigger is unrelated to exposing R & D specification/compliance knowledge.
* a REST API is too generic here; the scenario specifically points to the existing MCP server .
* a tool is close conceptually, but the most direct answer choice tied to the existing R & D technology is the MCP server .
NEW QUESTION # 28
A company has a Microsoft Foundry generative Al model.
You need to evaluate the model ' s output to measure the overall quality and coherence of generated responses. The evaluation must use GPT-4o as a judge and return a numeric score for each output.
Which type of metric should you use?
Answer: B
NEW QUESTION # 29
Hotspot Question
A company plans to implement an AI solution that will contain a Microsoft Copilot Studio agent and a Microsoft Foundry agent. The solution will be stored in a source code repository.
You need to recommend a deployment method for each agent. The solution must meet the following requirements:
- A test environment must be used before a deployment to production.
- Production must be isolated from development and testing.
- The deployment must be repeatable and fully automated.
- The solution must NOT require manual intervention.
Which deployment method should you recommend for each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Use a Microsoft Power Platform deployment pipeline
Copilot Studio agent
The preferred deployment method is to use a Microsoft Power Platform deployment pipeline.
Microsoft Power Platform pipelines are specifically designed to meet your requirements for a secure, automated, and repeatable application lifecycle management (ALM) process for Copilot Studio agents:
Box 2: Use a Bicep file
Microsoft Foundry agent
In the scenario described for a Microsoft Foundry agent, the preferred deployment method is to use a Bicep file.
This approach is best suited for your requirements because:
Infrastructure as Code (IaC): Azure Bicep allows you to define your entire environment-including the Foundry hub, projects, and model deployments-as code stored in your source repository.
Automation & Repeatability: Bicep files integrate directly with GitHub Actions or Azure Pipelines, enabling fully automated, repeatable deployments without manual intervention.
Environment Isolation: You can use Bicep to provision distinct, isolated resources for development, testing, and production by parameterizing the deployment for each environment.
Suitability: While Power Platform pipelines are used for Copilot Studio agents, Foundry-based agents are Azure resources where Bicep is the native and more powerful automation tool for managing the underlying infrastructure and model endpoints.
Reference:
https://learn.microsoft.com/en-us/power-platform/release-plan/2024wave2/microsoft-copilot- studio/solution-management-copilot-studio
https://techcommunity.microsoft.com/blog/azureinfrastructureblog/automating-azure-ai-foundry- deployment-with-iac-leveraging-bicep-and-github-work/4412155
NEW QUESTION # 30
Case Study 2 - Contoso, Ltd
Overview
Contoso, Ltd. is a high-tech manufacturing company that uses Microsoft Dynamics 365 Finance.
Dynamics 365 Supply Chain Management, and Dynamics 365 Commerce for its North American operations. The company designs and develops innovative products that have many patents and proprietary technologies. The patents and engineering designs are closely guarded secrets.
Contoso executives want to integrate and adopt AI solutions to help scale the company in preparation for an anticipated period of rapid growth.
The company has multiple legal entities and Azure subscriptions that will be used in the adopted AI solutions.
Requirements
AI Adoption
The following executives will have specific responsibilities in the overall AI adoption:
- Chief Technology Officer (CTO): Select one Dynamics 365 Finance,
Dynamics 365 Supply Chain Management or Dynamics 365 Commerce prebuilt
AI agent and one custom Microsoft Copilot Studio AI agent to prioritize and deploy during the initial AI adoption phase.
- Chief Information Officer (CIO): Ensure that appropriate security
labels are assigned to the data used by the AI agents.
- Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the AI agents being deployed.
- Chief Information Security Officer (CISO): Discover and inventory AI
resources for auditing.
- Chief Executive Officer (CEO): Ensure that all solutions adhere to
industry-standard responsible AI practices.
All AI initiatives and agents will have a detailed business use case, a defined audience profile, and an estimated ROI that will compare the cost savings of the current process against the estimated costs of using the new AI solutions.
The company's research and development (R&D) department already has a custom Model Context Protocol (MCP) server that contains comprehensive product specifications and compliance data.
Prebuilt AI Agent
The CTO has NOT yet selected which prebuilt AI agent to use in Dynamics 365 Supply Chain Management. The CTO wants to view available agent templates to identify which agent will add the most business value.
Depending on which high-priority AI agents are identified, its agent capabilities must be previewed in a discovery meeting with the relevant business operation stakeholders.
Custom AI Agent
Contoso has identified the following custom AI agent requirements:
- The custom AI agent will use data from Dynamics 365 Supply Chain
Management to answer questions for the manufacturing team as a low-code solution.
- The custom AI agent will be accessible from within Microsoft Teams.
- The custom AI agent must be designed to eventually connect to other
agents that can be selected based on their description.
- The topics used in the custom AI agent will be selected based NOT on
a trigger phrase, but on a description of the purpose of the query, to
make the interactions more conversational.
- The custom AI agent must be able to answer questions about product
specifications by using existing technologies. The product
specifications are maintained by the R&D department.
- The custom AI agent must be integrated with and accessible from
Dynamics 365 Supply Chain Management.
- The custom AI agent must be able to use Dynamics 365 Supply Chain
Management business logic that is stored outside of the application.
Analysis, Reporting, and Troubleshooting
Contoso has identified the following analysis, reporting, and troubleshooting requirements:
- The CISO will audit all the AI solutions monthly for compliance and
security.
- The CFO will analyze all the AI solutions quarterly to compare the
estimated ROI against actual measured efficiencies and adoption. The
CFO will use the Copilot Studio agent usage estimator to perform this
analysis.
- The CISO wants to identify how much sensitive data was accessed for a given AI agent run and who accessed the data. Too much sensitive data accessed by a single user might indicate a high security risk.
- The CTO wants to track user feedback on the quality of the AI agent
responses during user interactions with the agents. Consistently poor
feedback will trigger an escalated reengineering discussion.
- The CEO wants a quarterly assessment of all the required metrics for
their specific responsibilities. The tools used for the assessments
must be Microsoft-recommended and must verify reliability,
interpretability, fairness, and compliance.
- The CFO wants to identify how many interactions with the AI agents
are abandoned on a given day as compared to resolved conversations. Too many abandoned sessions might indicate that Copilot Studio credits are being used inefficiently by end users.
Drag and Drop Question
Which tools should you recommend to assist the CISO and the CIO with their specific responsibilities? To answer, drag the appropriate tools to the correct executives. Each tool may be used once, more than once, or 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.
Answer:
Explanation:
NEW QUESTION # 31
A company has an AI business solution that uses Microsoft Copilot Studio agents.
You need to recommend prompt best practices to improve the effectiveness of agent interactions.
Which two actions should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
To maximize the effectiveness of Microsoft Copilot Studio agents through prompt engineering, the two best practices you mentioned are critical, along with several key refinements:
1. Use Clear and Specific Instructions in Prompts
Define Goal, Context, and Persona: Provide a clear objective (what to do), necessary context (background information), and a defined persona (e.g., "You are an HR assistant...") to set the tone.
Be Specific and Actionable: Use precise verbs like "search," "summarize," "draft," or "list." Avoid vague instructions. For example, instead of "tell me about sales," use "Summarize Q3 sales performance in the North region".
Use Structured Formats: Utilize Markdown (headings, bullet points, numbered lists) to make instructions easy to scan and parse. Organize instructions into sections like # PURPOSE, ## RESTRICTIONS, and ## ACTIONS.
Provide Examples (Few-Shot Prompting): Include examples of inputs and desired outputs to help the agent understand complex tasks or required formatting.
Define Constraints: Explicitly state what the agent should not do, what information to exclude, or when to decline a request.
2. Regularly Test and Refine Prompts Based on User Input
Test within Copilot Studio: Utilize the testing pane to simulate user queries and review agent responses. Experiment with different prompt variations to see which yields the best result.
Use Prompt Advisor: Leverage the Prompt Advisor feature to get a confidence score and feedback on your prompts, which helps identify areas for improvement.
Review Analytics: Regularly check conversation logs, customer feedback (thumbs up/down), and user queries in the analytics dashboard to find where the agent struggles.
Iterate and Improve: Treat prompting as a conversation with the agent. If the first output isn't perfect, refine the instructions, add more context, and try again.
Monitor Performance (Red Teaming): Test edge cases and potential failure modes to ensure the agent remains reliable and secure.
By following these practices, you can make agent behavior more predictable, accurate, and useful for end-users Reference:
https://www.preciofishbone.com/knowledge-hub/prompting-and-instruction-tips-optimize-copilot-agents/
NEW QUESTION # 32
......
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