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| Section | Weight | Objectives |
|---|---|---|
| Deploy and Monitor Agentic AI Solutions | 20-25% | - Optimize agent performance - Publish and deploy agent solutions - Implement error handling and logging - Configure monitoring and analytics |
| Implement Agentic AI Solutions | 30-35% | - Configure security and compliance settings - Configure AI agents using Microsoft Copilot Studio - Implement data connections and plugins - Set up agent-to-agent communication |
| Design Agentic AI Solutions | 30-35% | - Define agent capabilities and boundaries - Select appropriate AI models and services - Design multi-agent architectures - Identify business scenarios for agentic AI |
| Governance and Best Practices | 10-15% | - Ensure regulatory compliance - Apply responsible AI principles - Implement data privacy and security controls |
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NEW QUESTION # 93
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 # 94
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 # 95
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?
Answer: A
Explanation:
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 Why the other options are less suitable:
A). Chunk documents during indexing can improve retrieval quality in some RAG scenarios, but it does not directly guarantee lower total retrieved token volume in the final prompt B). Lower the maximum token usage limit for the responses may reduce output length, but it does not solve excessive input-context usage and can hurt response quality C). Reduce the number of knowledge sources used by the agent is too blunt and may remove useful grounding unnecessarily
NEW QUESTION # 96
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: D
Explanation:
The requirement says the evaluation must:
* measure overall quality and coherence of generated responses
* use GPT-4o as a judge
* return a numeric score for each output
That maps directly to AI quality (AI assisted) .
Why D is correct:
AI-assisted quality metrics use a strong model such as GPT-4o to evaluate generated outputs against qualitative dimensions like coherence, relevance, completeness, and overall response quality, and then assign scores.
Why the other options are not correct:
* A. Groundedness checks whether the answer is supported by the source/context, not overall quality and coherence.
* B. AI quality (NLP) usually refers to traditional NLP-style metrics rather than judge-model scoring with GPT-4o.
* C. risk and safety evaluates harmfulness, toxicity, jailbreak risk, or policy concerns, not overall response quality.
NEW QUESTION # 97
Drag and Drop Question
A company has an AI solution that uses a Microsoft Copilot Studio agent.
You need to monitor the agent's performance. The solution must meet the following requirements:
- Monitor the agent's telemetry in near-real-time (NRT).
- Download transcripts of full conversations.
- Monitor the agent's usage and performance.
What should you use for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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: Application Insights
Monitor the agent's telemetry in near-real-time (NRT).
To monitor Microsoft Copilot Studio agent performance and telemetry in near-real-time (NRT), the best tool to use is Azure Application Insights.
By connecting your Copilot Studio agent to an Azure Application Insights instance, you can capture detailed runtime data, including user messages, topics triggered, and custom events as they happen.
Box 2: Microsoft Power Apps
Download transcripts of full conversations.
To monitor Microsoft Copilot Studio agent performance, you can download full conversation transcripts for the last 29 days using the Power Apps portal to analyze user interactions. Access Dataverse via Power Apps, navigate to tables, select ConversationTranscript, and export the data as a CSV file to identify engagement, resolution, and escalation patterns.
Steps to Download Transcripts via Power Apps:
1. Sign in to make.powerapps.com.
2. Select the correct environment in the top right corner.
3. In the side menu, select Tables (or Data > Entities in older views).
4. Search for and select the ConversationTranscript table.
5. Select Export data from the top menu.
6. Once compiled, select Download exported data to save the file.
Box 3: Copilot Studio
Monitor the agent's usage and performance.
To monitor a Microsoft Copilot Studio agent's usage and performance, you can use the built-in Analytics page in Copilot Studio, which provides comprehensive data on key metrics, conversation outcomes, and user satisfaction.
Reference:
https://reshmeeauckloo.com/posts/copilot-connecting-application-insights
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-overview
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-overview
NEW QUESTION # 98
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