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16. Frage
A financial services company uses Microsoft Dynamics 365 Finance.
Currently, the company ' s support staff manually reviews customer transaction histories to detect potential fraud cases before escalating the cases.
You need to recommend an automation solution for the review process. The solution must ensure that escalations reach a human analyst for final decision making. What should you recommend?
Antwort: D
17. Frage
You are creating validation criteria for a custom generative Al model that produces business reports based on internal enterprise data. You need to Assess whether the model ' s outputs are appropriate and meaningful (or the business reports. Which metric should you use?
Antwort: C
Begründung:
The correct evaluation criterion is alignment of the output to domain-specific tasks . The objective is to establish whether generated reports are semantically appropriate, useful, and correct for the intended business purpose. This requires evaluating output quality against the requirements and expectations of the specific business domain.
Microsoft ' s generative-AI evaluation guidance emphasizes that evaluation should reflect the nature of the task and dataset , and that domain-specific scenarios frequently require customized evaluation criteria.
Training duration and inference resource utilization are operational or performance measurements, not measures of whether the generated report is meaningful. Likewise, active-user count measures adoption rather than model-output quality.
18. Frage
Drag and Drop Question
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? To answer, drag the appropriate language models to the correct agents. Each language model 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.
Antwort:
Begründung:
Explanation:
Box 1: Natural Language Understanding (NLU)
Agent1 must support simple and short phrases for a given topic.
For a Microsoft Copilot Studio agent that must not use generative orchestration and requires support for simple, short trigger phrases, the best choice is the Classic NLU (Natural Language Understanding) model.
When you disable generative orchestration (also known as "Generative mode" or "Generative AI" orchestration), the agent reverts to Classic orchestration. In this mode, the agent relies on predefined trigger phrases to map user input directly to specific topics.
Box 2: Natural Language Understanding + (NLU +)
Agent2 must integrate with Microsoft Dynamics 365 Contact Center voice channel.
For a Microsoft Copilot Studio agent using classic orchestration (no generative orchestration) and integrating with the Dynamics 365 Contact Center voice channel, the best language model is NLU+.
Why NLU+ is the Best Choice
While standard agents offer three "classic" Natural Language Understanding (NLU) options, NLU+ is specifically designed for high-performance, enterprise-grade scenarios like voice channels.
Note:
Comparison of Classic Models
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-overview
19. Frage
Scenario: A development team is building a complex, multi-step agent using the Microsoft Agent Framework. This agent needs to receive a high-level user request, break it down into the required sequence of internal API calls and knowledge base, and manage the execution flow of these steps to achieve the final outcome.
Within the Agentic Core, which specific sub-component is primarily responsible for analyzing the user's intent, determining the optimal sequence of required tools/APIs and data sources (RAG), and managing the logical execution flow of these steps?
Antwort: D
Begründung:
The Planner and Orchestrator is correct because this component is the "brain" of the Agentic Core. Its function is to take the user prompt, use the LLM to assist in decomposition, build a logical plan (Planner) of steps to solve the request, and then manage the execution of those steps using the available Tools and data sources (Orchestrator).
References:
https://learn.microsoft.com/en-us/agent-framework/media/agent.svg
https://techcommunity.microsoft.com/blog/educatordeveloperblog/ai-agents-planning-and- orchestration-with-the-planning-design-pattern---part-7/4399204
https://www.microsoft.com/en-us/microsoft-365/planner/microsoft-planner
20. Frage
A company has a Microsoft Power Platform environment.
You need to build two agents named Agent1 and Agent2. The solution must meet the following requirements:
* Agent1 must be extendable by using the Semantic Kernel and must connect to multiple business apps and APIs.
* Agent2 must connect directly to data stored in Microsoft Dataverse and must be embeddable in a Microsoft Power Apps canvas a pp.
What should you use to build each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Agent1 # Microsoft Foundry
Agent2 # Copilot in Power Apps
Comprehensive and Detailed Explanation from Agentic AI Topics:
For Agent1, the requirement is that it must be extendable by using Semantic Kernel and connect to multiple business apps and APIs. The best fit is Microsoft Foundry because Foundry-based agents are designed for extensibility and developer-oriented orchestration, including integration patterns that work well with Semantic Kernel and external tools/APIs.
For Agent2, the requirement is that it must connect directly to Microsoft Dataverse and be embeddable in a Power Apps canvas app. The best fit is Copilot in Power Apps, because it is designed for Power Platform- native experiences, works naturally with Dataverse-backed app data, and is intended for embedding AI experiences inside canvas apps.
Why the other options are not the best match:
Azure Logic Apps is for workflow orchestration, not the primary platform for building these agents.
Microsoft Copilot Studio is strong for conversational agents, but the wording here points more directly to Power Apps-native embedding for Agent2 and Semantic Kernel extensibility for Agent1.
21. Frage
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