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질문 # 105
You are designing a Microsoft Copilot Studio agent that uses a custom Microsoft Foundry model to generate responses. You need to ensure that the agent can securely connect to and invoke the custom model during user interactions. What should you include in the design?
정답：D
질문 # 106
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.
정답：
설명：
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
질문 # 107
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.
Which two components for the custom AI agent should you include in the application lifecycle management (AIM) process? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
정답：C,D
설명：
[C]
A Microsoft Power Platform solution is the standard container for transporting and managing customizations across the Power Suite (Copilot Studio, Power Automate, and Power Apps). It perfectly aligns with the specific requirements.
[B]
Based on the application lifecycle management (ALM) process for a Microsoft Power Platform solution that integrates with Dynamics 365 Supply Chain Management, you should use a ZIP package.
Recommended ALM Component
In the Microsoft Power Platform ecosystem, the standard unit of deployment for ALM is a Solution, which is exported and transported as a ZIP package.
ZIP package: This is the standard format for Power Platform solutions containing Copilot Studio agents, Power Automate flows, and custom connectors used to bridge Dynamics 365 and Teams.
Incorrect:
[Not A]
Azure package: Typically refers to Azure-native resources; while used for custom coding, the Power Platform solution itself is handled via ZIP exports.
[Not D]
Cloud Scale Unit (CSU) package: Specifically for commerce or edge workloads, not for deploying general AI agents.
[Not E]
X++ model is used for core code changes inside Dynamics 365 Finance & Operations, but not for the low-code Power Platform/Copilot Studio orchestration described.
Reference:
https://thorprojects.com/2025/09/23/copilot-studio-power-on-the-power-platform/#
https://learn.microsoft.com/en-us/dynamics365/supply-chain/procurement/supplier-com-agent-setup
질문 # 108
A company uses Microsoft Dynamics 365 Sales to manage leads that are stored in a Microsoft Dataverse table named Lead and use non-standard terminology and custom columns.
You need to configure business terms in the Lead table so that Microsoft Copilot controls can summarize the leads efficiently. The solution must minimize administrative effort.
How should you configure the business terms?
정답：A
설명：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is B. Map the field display names as business terms .
In this scenario, the company is using Microsoft Dynamics 365 Sales with data stored in a Dataverse Lead table , but the table includes non-standard terminology and custom columns . The goal is to help Microsoft Copilot controls understand and summarize lead data efficiently, while also minimizing administrative effort .
This points directly to using the field display names as business terms .
Why this is the best choice:
Microsoft Copilot works best when the business meaning of data is clear and human-readable. In Dataverse, display names are already designed to represent the user-friendly business meaning of a field. They are what users see in the interface, and they typically align better with business language than technical names do.
By mapping field display names as business terms, you achieve two important outcomes:
* Copilot can interpret the fields more naturally Since display names reflect business-friendly terminology, Copilot can generate summaries that make sense in the context of sales operations.
* Administrative effort stays low The display names already exist. Reusing them avoids creating a large amount of extra metadata or manually defining separate business terms for every column.
From an AI-powered business solutions perspective, this is exactly the kind of configuration choice that improves semantic clarity while keeping the solution lightweight and maintainable . In enterprise AI adoption, the best design is often the one that improves model comprehension without adding unnecessary governance overhead.
Why the other options are incorrect:
A). Combine all the fields into one custom field
This is not a good design for Dataverse, Dynamics 365, or Copilot summarization. Combining everything into one field reduces structure, hurts maintainability, makes reporting harder, and weakens Copilot's ability to understand the meaning of specific attributes. Structured data is far more useful than a merged blob of text.
C). Add the schema names as business terms
Schema names are technical identifiers, not business-friendly labels. They are often formatted for developers and administrators rather than end users. Copilot summarization benefits from semantic clarity, and schema names usually do not provide that. They also do not minimize interpretation effort from a business AI perspective.
D). Create new business terms for each field
This might work technically, but it does not minimize administrative effort. The question explicitly asks for a solution that keeps administration low. If display names already represent the business meaning well enough, creating entirely new business terms for every field would be unnecessary overhead.
Expert reasoning:
In Microsoft AI business applications, especially with Dataverse + Dynamics 365 + Copilot , the system performs better when field metadata is aligned to business-readable language . The most efficient way to do that is usually to reuse display names , because they already express the intended meaning of the data and avoid extra manual configuration.
질문 # 109
A company has a Microsoft Copilot Studio agent for customer support. You are reviewing and validating the following prompts:
* A prompt that has instructions to " help the customer as best you can "
* A prompt that helps retrieve product information from a knowledge base You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
This question is about improving prompt quality so a Microsoft Copilot Studio agent gives consistent and accurate answers.
For the first prompt, "help the customer as best you can" is too vague. It does not tell the model exactly what task to perform, what boundaries to follow, or what kind of response is expected. The correct improvement is to rewrite the prompt with clear and task-specific instructions . Clear prompts reduce ambiguity and make agent behavior more predictable and repeatable.
For the second prompt, the agent is retrieving product information from a knowledge base . To keep answers accurate and grounded, the best practice is to use responses with only reference sources and limit the response scope . That ensures the model stays tied to approved knowledge and does not invent unsupported product details.
Why the other options are not correct:
* Add filler words to make the prompt sound more natural and conversational does not improve accuracy or consistency.
* Keep the prompt vague to enable model flexibility increases inconsistency.
* Add several open-ended questions to give the model broader context can make responses less focused.
* Remove the knowledge source so that the model responds freely with general product information would reduce reliability and increase hallucination risk.
질문 # 110
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