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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan AI-powered business solutions | 25% | - AI solution design strategy
|
| Topic 2: Design AI-powered business solutions | 30% | - Extensibility and orchestration
|
| Topic 3: Deploy AI-powered business solutions | 45% | - Testing and validation
|
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NEW QUESTION # 37
Your company is deploying customer support agents in Microsoft Foundry for a claims business group that uses sensitive grounding data The group has a production project that connects to a data source by using a project-scoped connection that uses Microsoft Entra pass through authentication.
The solution has the following requirements:
* For grounding data changes, the company must log data ingestion events, sensitivity label changes, and data movement across geographical regions. Audit records must preserve traceability for investigations, and only capture metadata.
* The solution must use a DataZone deployment in a Foundry resource located in the United States for the Azure Direct.
Answer:
Explanation:
Explanation:
* DataZone deployment # Yes
* Full grounding documents in audit log # No
* Microsoft Entra passthrough authentication # Yes
A DataZone deployment created in a Microsoft Foundry resource located in the United States processes prompts and responses within the United States data zone . Microsoft states that DataZone deployments restrict inference processing to the specified data zone, unlike Global deployments, which can process requests across supported Azure geographies. Therefore, the described US DataZone deployment satisfies the prompt-and-response processing residency requirement.
The second statement is No . The requirement explicitly specifies that audit records must capture metadata only while preserving traceability of ingestion events, sensitivity-label changes, and geographical data movement. Storing the complete grounding documents would place actual sensitive content in the audit trail and directly violate that requirement. Audit information should therefore identify relevant events and contextual metadata without duplicating the grounding content itself.
The third statement is Yes . Microsoft Entra identity passthrough allows access to be evaluated using the individual user ' s identity and permissions , rather than a common project credential. Microsoft recommends user-token/Entra passthrough specifically when sensitive data sources require greater per-user access control.
NEW QUESTION # 38
A company uses Microsoft Dynamics 365 to manage service operations. Dispatchers coordinate service requests, and technicians perform scheduled on-site work.
You need to design a solution that will use Microsoft Copilot to improve the efficiency of the service operations. The solution must meet the following requirements:
* Provide Al-driven assistance to help staff organize and resolve work orders.
* Deliver contextual Al support to frontline workers as they prepare for and complete customer appointments.
Which two components should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,F
Explanation:
This scenario is centered on Dynamics 365 service operations , with two distinct user groups:
* dispatchers/staff who organize and resolve work orders
* frontline technicians who perform on-site service appointments
The best two components are:
* Copilot in Field Service
* Dynamics 365 Field Service mobile app
Why C. Copilot in Field Service is correct:
Copilot in Field Service is designed to help service teams work more efficiently with work orders, scheduling context, task assistance, and service-related operational support . This matches the requirement to provide AI-driven assistance to help staff organize and resolve work orders .
Why D. the Dynamics 365 Field Service mobile app is correct:
Frontline workers and technicians use the Field Service mobile app while preparing for and completing appointments. That is the right surface for delivering contextual AI support in the flow of field work .
Why the other options are not the best fit:
* A. Copilot in Customer Service is focused more on customer support agents than on field dispatch
/service execution.
* B. Copilot in Outlook is too generic and not purpose-built for field service operations.
* E. Dynamics 365 Customer Service is not the primary app for technician appointment execution.
* F. Copilot Service workspace is more aligned with service agents in customer support environments rather than frontline field technicians.
NEW QUESTION # 39
Your customer needs their custom AI agent to interact seamlessly and securely with multiple internal enterprise systems, including their ERP, CRM, and various legacy order processing APIs.
They are looking for a standardized, future-proof method for this integration that minimizes the need for developing and maintaining bespoke, fragile custom connectors for every single endpoint.
Based on Microsoft's recommended guidance for agent interoperability, which integration approach should you implement to achieve this standardized and robust cross-system communication?
Answer: C
Explanation:
Implement the Model Context Protocol (MCP) by exposing the backend REST APIs as MCP servers is correct because the Model Context Protocol (MCP) is Microsoft's strategic initiative designed to provide a standardized, universal protocol for AI agents to communicate with tools, services, and other agents. By exposing existing REST APIs as MCP servers, the customer can achieve standardized, robust, and future-proof interoperability for their AI agent across diverse internal systems, avoiding the pitfalls of custom, one-off connectors.
References:
https://learn.microsoft.com/en-us/azure/api-management/export-rest-mcp-server
https://learn.microsoft.com/en-us/microsoft-copilot-studio/agent-extend-action-mcp
NEW QUESTION # 40
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.
Answer:
Explanation:
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.
NEW QUESTION # 41
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?
Answer: B
Explanation:
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.
NEW QUESTION # 42
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