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질문 # 79
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.
정답：
설명：
Explanation:
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.
질문 # 80
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
정답：A
설명：
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size
질문 # 81
You need to recommend a Microsoft Power Platform solution for customer support. The solution must include Al capabilities in Microsoft Power Automate and must meet the following requirements:
* Use a centralized workspace for Al models.
* Generate short overviews from large amounts of unstructured text such as case notes or transcripts, without requiring additional training or coding.
What should you include in the recommendation for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
1. Centralized Workspace for AI Models
In the evolving Microsoft AI ecosystem, Azure AI Foundry (formerly Azure AI Studio) serves as the unified, centralized workspace for developers and organizations to build, manage, and deploy AI models.
* Unified Hub: It provides a single location to access the Model Catalog, manage deployments, and perform prompt engineering.
* Integration: While AI Builder is the " front-end " for citizen developers in Power Automate, Azure AI Foundry is the foundational " engine room " where the underlying models (including those used by prompts) are managed and governed at an enterprise level.
* Scalability: It allows organizations to host their own custom models or utilize industry-leading foundation models (like GPT-4o) which then power the downstream capabilities in the Power Platform.
2. Generating Short Overviews Without Training or Coding
To summarize large amounts of unstructured text like case notes or transcripts without manual effort, AI Builder prebuilt prompts are the specific solution.
* AI Builder Prebuilt Prompts: These are ready-to-use GPT-based instructions designed for common business tasks. Specifically, the " AISummarize " prebuilt prompt is engineered to take input text and generate a concise summary automatically.
* Zero Training Required: Unlike " Custom Models " which require you to provide sets of historical data to teach the AI, prebuilt prompts leverage pre-trained Large Language Models (LLMs). This meets the requirement of " no additional training or coding. "
* Ease of Use: In Power Automate, you simply add the " Create text with GPT using a prompt " action and select the prebuilt summary prompt, passing it the text from your case notes or transcripts.
질문 # 82
A company uses Microsoft Dynamics 365 Finance for accounts payable and customer debt recovery. You are designing an Al finance process that meets the following requirements:
* Provides Al-driven details to help staff identify overdue vendor invoices and outstanding balances
* Helps staff reduce how long it takes to review overdue invoices and payment history You need to recommend which Microsoft Copilot features to include in the design.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Vendor overdue invoices and outstanding balances # AI Summaries with Copilot Review overdue invoices and payment history faster # Collections coordinator summary This question combines two different finance-process needs inside Dynamics 365 Finance :
The first requirement is in the accounts payable / vendor context. Staff need AI-driven details to quickly understand overdue vendor invoices and outstanding balances. The best fit is AI Summaries with Copilot , because this feature is intended to surface concise, contextual summaries that help users understand finance records faster without manually opening and reviewing every transaction.
The second requirement is in the customer debt recovery / collections context. Staff need to reduce the time spent reviewing overdue invoices and payment history . The best match here is Collections coordinator summary , because it is specifically aligned to collections work and helps users quickly review the customer' s collections status, invoice aging, and payment-related context.
Why the other options are less suitable:
* Agent management is administrative and not the feature that provides invoice/balance insights.
* The Account Reconciliation agent is more focused on reconciliation tasks than on these specific AP and collections-review needs.
* The supplier Communication agent is oriented toward supplier interaction/communication, not primarily summarizing overdue invoice and balance insight for internal review.
* Analyze demand plans with Copilot is unrelated to overdue invoices or payment-history review.
질문 # 83
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.
정답：A,B
설명：
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/
질문 # 84
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