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NEW QUESTION # 97
A company has a Microsoft Copilot Studio agent that has been in production for three months.
The agent has received positive feedback from users.
You need to identify the number of questions unanswered by the agent and the number of abandoned sessions between the users and the agent.
Which Copilot Studio insights should you use? To answer, drag the appropriate insights to the correct requirements. Each insight may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The number of unanswered questions # Generated answer rate and quality; The number of abandoned sessions # Conversation outcomes Why "Generated answer rate and quality" is correct The requirement is to identify the number of questions unanswered by the agent. In Copilot Studio, unanswered-question behavior is tied to how often the agent successfully generates answers and the quality of those answers.
The Generated answer rate and quality insight is the right place to evaluate whether the agent is:
answering user questions
failing to generate answers
producing low-quality responses
missing knowledge coverage
From an AI business solutions standpoint, unanswered questions are a direct signal of knowledge gaps, grounding gaps, or prompt-response weaknesses. This is exactly what generated answer analytics are meant to surface.
Why "Conversation outcomes" is correct
The requirement also asks for the number of abandoned sessions between users and the agent.
Abandonment is a conversation-level outcome, not a reaction or survey result. The Conversation outcomes insight tracks what happened to the interaction, such as whether the conversation was:
resolved
escalated
abandoned
otherwise completed unsuccessfully
That makes it the correct metric for identifying abandoned sessions.
Why the other insights are not correct
Reactions
Reactions capture signals like positive or negative user feedback, but they do not directly measure unanswered questions or abandoned sessions.
Survey results
Survey results reflect user satisfaction feedback after interactions, but they do not directly quantify unanswered questions or abandonment counts.
NEW QUESTION # 98
Drag and Drop Question
A company has a Microsoft Foundry project that uses a single agent and a single prompt to complete a series of tasks.
The agent encounters the following issues:
- It frequently produces incomplete results.
- It struggles with domain-specific reasoning.
- Agent response times are remarkably slow.
You need to recommend a solution to improve the overall performance and accuracy of the agent.
What should you include in the recommendation? To answer, drag the appropriate actions to the correct requirements. Each action 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:
Explanation:
Box 1: Move to a multi-agent architecture
To improve performance
Moving to a multi-agent architecture in Azure AI Foundry is a highly effective strategy to overcome performance bottlenecks, as single-agent systems often struggle with long-running tasks, leading to high latency and timeout issues. By decomposing complex tasks into smaller, specialized subtasks, you can improve response times through parallel processing and targeted tool usage.
Incorrect:
* Upgrade to a larger generative AI model
To address slow response times in your Microsoft Foundry agent, upgrading to a larger generative AI model is one option, but it may increase latency in some scenarios due to higher processing demands. Instead, a combination of prompt optimization, model selection, and architectural changes in Microsoft Foundry is recommended to improve performance.
Box 2: Add a grounding data source
To improve accuracy
To improve the performance of an agent in a Microsoft Foundry project experiencing incomplete results, weak domain reasoning, and high latency, adding a grounding data source is a highly effective strategy. Grounding connects the Large Language Model (LLM) to verified external data, ensuring responses are accurate, contextual, and less likely to hallucinate.
Reference:
https://developer.microsoft.com/blog/designing-multi-agent-intelligence
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ground-your-ai-agents-with- knowledge-from-bing-search-microsoft-fabric-sharepoin/4303634
NEW QUESTION # 99
You are designing two Microsoft Copilot Studio agents named Agent1 and Agent2. Each agent must meet the following requirements:
Each agent must use a standard model.
Each agent must NOT use generative orchestration.
Agent1 must support simple and short phrases for a given topic.
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.
You need to recommend language models for the agents.
What should you recommend for each agent?
Answer:
Explanation:
Explanation:
Agent 1 = NLU
Agent 2 = NLU and NLU+
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview Agent1 must support simple and short phrases for a given topic . That is the classic use case for NLU in Copilot Studio. NLU is designed for standard intent recognition where users enter brief, predictable utterances tied to a topic.
This makes NLU the best fit for:
* narrow topic triggering
* short phrase matching
* standard, non-generative agent behavior
Why Agent2 = NLU+
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel . For that scenario, NLU+ is the correct recommendation among the listed standard models.
NLU+ extends the standard NLU approach and is the model aligned to scenarios that need stronger language understanding support in more advanced enterprise channel integrations such as voice experiences. Since the requirement explicitly says:
* use a standard model
* do not use generative orchestration
NLU+ fits better than Azure OpenAI or other generative options.
NEW QUESTION # 100
You are designing a testing solution for Microsoft Copilot Studio agents.
You need to validate prompt engineering best practices to ensure that the agents generate accurate and contextually relevant responses. Which prompt validation techniques and metrics should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To validate prompt engineering for Microsoft Copilot Studio agents, the goal is to confirm that the agent responds correctly even when users ask the same thing in different ways, and to measure whether the responses are actually useful and correct.
For the prompt validation technique, the correct choice is Use prompts that have varied phrasing. This is a core best practice because real users do not ask questions in one fixed form. They may use different wording, sentence structure, synonyms, or levels of detail. Testing with varied phrasing checks whether the prompt design is robust and whether the agent can still produce the right response across natural language variation.
For the metric, the correct choice is Response relevance and accuracy. Since the requirement is to ensure responses are accurate and contextually relevant, this is the most appropriate measure. It directly evaluates whether the output answers the user's need correctly and in the right context.
Why the other options are not correct:
Exclude domain-specific terminology from the prompts is not a best practice in business AI solutions. In many enterprise scenarios, domain-specific terms are essential for accuracy.
Use only simple, one-word prompts does not reflect real-world usage and would weaken testing coverage.
The number of words generated per response does not tell you whether the response is correct or contextually appropriate.
The response generation time is a performance metric, not the best metric for validating prompt quality.
NEW QUESTION # 101
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.
What should you configure for the custom AI agent?
Answer: A
Explanation:
Generative orchestration is the most appropriate choice for this Microsoft Dynamics 365 AI agent solution.
This selection directly addresses your requirements for a low-code, conversational, and interconnected agent ecosystem within the Microsoft Power Platform and Dynamics 365 environment.
Why Generative Orchestration?
Generative orchestration (available in Microsoft Copilot Studio) is specifically designed to move away from rigid, trigger-phrase-based logic toward a flexible, intent-based model.
Mapping to Your Requirements
Intent-Based Selection: Unlike "Classic" orchestration which relies on exact trigger phrases, generative orchestration uses Natural Language Understanding (NLU). It selects the correct topic or "sub-agent" based on a description of the purpose, allowing for the conversational flow you requested.
Low-Code Integration: Copilot Studio is the primary low-code tool for Dynamics 365. It provides native connectors to Supply Chain Management (SCM) data and can be embedded directly into the SCM interface or deployed to Microsoft Teams.
External Business Logic: It can trigger Power Automate flows or API calls to execute business logic stored in external databases or legacy systems, bringing that data back into the conversation.
Product Specifications: By using Generative Answers, the agent can crawl "existing technologies" like SharePoint libraries, internal wikis, or SCM data tables to answer complex spec questions without manual topic authoring.
Incorrect:
[Not A]
AI-Assisted Evaluators are testing and diagnostic tools, not runtime execution engines.
You would use these to measure how well your agent is performing, but they cannot be the agent or manage the logic flow.
[Not B]
Classic Orchestration is entirely dependent on trigger phrases.
It creates a "command-and-control" feel rather than the fluid, conversational interaction you are looking for. It also scales poorly when trying to connect multiple agents.
[Not D]
Azure OpenAI Models (Reasoning Models) while powerful, this is a pro-code path (API-heavy).
Using raw Azure OpenAI models would require significant custom development, missing the
"low-code" requirement. While Generative Orchestration uses these models under the hood, the orchestration layer itself is what manages the "which agent to call" logic.
Scenario: 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.
Reference:
https://www.syncfusion.com/blogs/post/integrating-ai-into-your-apps-with-ai-builder
NEW QUESTION # 102
......
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