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NEW QUESTION # 93
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 # 94
Which framework should you use for the infrastructure migration?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is A. Microsoft Cloud Adoption Framework for Azure.
In this scenario, Fabrikam is moving from a fully on-premises environment to a completely cloud-based topology, and the requirements explicitly state that:
Azure must be used for all future infrastructure workloads
the company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud That wording points directly to the Microsoft Cloud Adoption Framework for Azure.
The Cloud Adoption Framework is Microsoft's primary guidance for planning and executing enterprise cloud migrations. It is not just a technical migration checklist; it is a full strategic framework that helps organizations move workloads to Azure in a structured and governed way. It covers key areas such as:
migration strategy
readiness assessment
governance
identity and security planning
landing zones
operations management
organizational alignment
adoption planning
From an AI-powered business solutions perspective, this matters because AI solutions only succeed when the underlying infrastructure is modernized correctly. Fabrikam's goals include cloud migration, Dataverse consolidation, Dynamics 365 Sales adoption, and AI-first business operations. All of these depend on a strong Azure-based cloud foundation. The Cloud Adoption Framework helps ensure that this transformation is done in a way that is scalable, secure, and aligned to business outcomes.
Why the other options are incorrect:
B). Success by Design
This framework is mainly used for designing and implementing business applications in a way that improves adoption, usability, and solution quality. It is highly relevant for conversational experiences, application rollout, and ensuring users get value from solutions such as Dynamics 365 and Copilot-related implementations. However, it is not the primary framework for infrastructure migration to Azure.
C). Microsoft Power Platform Center of Excellence (CoE)
The CoE is used to establish governance, monitoring, best practices, and enablement for Power Platform usage across an organization. It helps manage makers, apps, flows, and adoption across Power Platform environments. While it is useful after adoption begins, it is not the framework used to migrate enterprise infrastructure to Azure.
D). Microsoft Power Platform Project Setup Wizard
This is more of a project setup and configuration aid for Power Platform implementations. It does not provide enterprise-scale migration guidance for moving an organization's infrastructure from on-premises systems to Azure.
Expert reasoning:
When a question mentions infrastructure migration, Azure, and Microsoft-recommended methodology, the best match is almost always the Microsoft Cloud Adoption Framework for Azure. In Microsoft solution architecture and AI business transformation scenarios, this framework is the standard answer for cloud migration planning and execution.
Category Breakdown
Category Number of Questions
Plan AI-powered business solutions 29
Deploy AI-powered business solutions 44
Design AI-powered business solutions 32
TOTAL 105
Exam Topic Breakdown
Exam Topic Number of Questions
Topic 2, Contoso. LtdOverview
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 Al 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 Al solutions.
AI Adoption
The following executives will have specific responsibilities in the overall Al adoption:
* Chief Technology Officer (CTO): Select one Dynamics 365 Finance, Dynamics 365 Supply Chain Management, or Dynamics 365 Commerce prebuilt Al agent and one custom Microsoft Copilot Studio Al agent to prioritize and deploy during the initial Al adoption phase.
* Chief Information Officer (CIO): Ensure that appropriate security labels are assigned to the data used by the Al agents
* Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the Al agents being deployed.
* Chief Information Security Officer (CISO): Discover and inventory Al resources for auditing.
* Chief Executive Officer (CEO): Ensure that all solutions adhere to industry-standard responsible Al practices.
All Al 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 Al 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 Al 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 Al 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 Al agent requirements:
* The custom Al agent will use data from Dynamics 365 Supply Chain Management to answer questions for the manufacturing team as a low-code solution.
* The custom Al agent will be accessible from within Microsoft Teams.
* The custom Al agent must be designed to eventually connect to other agents that can be selected based on their description.
* The topics used in the custom Al 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 Al 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 Al agent must be integrated with and accessible from Dynamics 365 Supply Chain Management.
* The custom Al 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 analysts, reporting, and troubleshooting requirements:
* The CISO will audit all the Al solutions monthly for compliance and security.
* The CFO will analyze all the Al 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 Al 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 Al 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 Al 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.
Case study question
NEW QUESTION # 95
You need to design a Microsoft 365 Copilot solution to optimize employee productivity. The solution must meet the following requirements:
Ensure that the employees can query content stored in a subset of Microsoft SharePoint Online sites and in Teams by using natural language-based prompt actions.
Ensure that employees receive contextually relevant responses in Microsoft 365 Copilot.
What should you include in the design?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is D. Configure Microsoft Graph access .
Microsoft 365 Copilot grounds its responses in Microsoft 365 data through the Microsoft Graph . If employees need to query content from a subset of SharePoint Online sites and Teams using natural- language prompts, the solution must ensure Copilot can access and use the right Microsoft 365 content context through Graph-connected permissions and data access patterns.
Why D is correct
Microsoft Graph is the core data and context layer for Microsoft 365 Copilot. It connects Copilot to organizational content such as:
* SharePoint sites
* Teams messages and files
* OneDrive content
* Outlook data
* calendar and collaboration context
Because the requirement is to provide contextually relevant responses in Microsoft 365 Copilot, the design must rely on the platform's native grounding mechanism. That mechanism is Graph-based access to Microsoft
365 content.
From an AI business solutions perspective, this is the right design because it ensures:
* natural-language prompts can retrieve relevant organizational knowledge
* responses are grounded in authorized enterprise content
* access remains aligned to Microsoft 365 permissions
* employees only see content they are allowed to access
This is especially important when only a subset of SharePoint sites should be included. The relevance and security model depend on the Microsoft 365 content graph and its permission-aware access behavior.
Why the other options are incorrect
A). Build a Microsoft Power Automate desktop flow to read the SharePoint content and post the responses to Teams This is not how Microsoft 365 Copilot should be designed for grounding enterprise content. It is overly manual, indirect, and does not provide native contextual grounding for Copilot responses.
B). Modify SharePoint settings
SharePoint settings may affect site permissions or content availability, but they do not by themselves enable Microsoft 365 Copilot's natural-language grounding across SharePoint and Teams.
C). Create a custom REST API that crawls the SharePoint content
This adds unnecessary custom complexity and bypasses the native Microsoft 365 Copilot architecture. The requirement is best met through Microsoft Graph-based access, not by building a parallel crawler.
Expert reasoning
For Microsoft 365 Copilot questions:
* if the requirement is to query Microsoft 365 content with natural language
* and return contextually relevant responses from SharePoint and Teams
* the key design element is usually Microsoft Graph
NEW QUESTION # 96
A company uses Azure OpenAI models that use grounding data from Microsoft Fabric for agents. The models are fine-tuned by using proprietary datasets.
You need to design a governance solution that meets the following requirements:
Restricts access to the grounding data to only assigned roles
Restricts model fine-tuning to only the AI engineering team
What should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Restricts access to grounding data # Microsoft Purview access policies; Restricts model fine-tuning # Role- based access control (RBAC) in Microsoft Foundry Why Microsoft Purview access policies is correct The grounding data is stored in Microsoft Fabric, and the requirement is to restrict access to that data to only assigned roles.
That is a data governance and access control requirement. Microsoft Purview access policies are the best fit because they are designed to govern and control access to data across enterprise data estates. In this case, they help ensure that only authorized roles can access the grounding data used by the agents.
From an AI business solutions perspective, grounding data is often one of the most sensitive parts of the solution because it can contain:
proprietary business knowledge
internal documents
regulated operational information
contextual data used to shape model outputs
Purview helps enforce governed access to that data layer rather than relying only on general infrastructure controls.
Why RBAC in Microsoft Foundry is correct
The second requirement is to ensure that only the AI engineering team can perform model fine-tuning.
That is an action-level platform permission requirement. The best control for that is role-based access control (RBAC) in Microsoft Foundry.
RBAC allows the organization to assign permissions based on job function, so only authorized users or groups can:
create or modify fine-tuning jobs
manage model assets
update training configurations
control deployment-related AI resources
This is the right governance pattern because fine-tuning changes model behavior and can introduce:
security risk
compliance risk
quality drift
misuse of proprietary datasets
Restricting that capability to the AI engineering team through RBAC creates a clear separation of duties.
Why the other options are incorrect
Azure AI Content Safety
This is used to detect and filter harmful content. It does not control access to Fabric grounding data.
Azure Monitor alerts
Alerts help observe activity, but they do not enforce role-based access to data.
Azure Policy compliance rules
Azure Policy is useful for enforcing resource configuration standards, but it is not the best answer for role- based access to Fabric grounding data or for limiting fine-tuning actions to a specific team.
Azure Resource Manager (ARM) resource locks
Resource locks help prevent deletion or modification of Azure resources, but they do not provide the right permission model for controlling who can perform model fine-tuning operations.
Microsoft Entra Conditional Access
Conditional Access is mainly about sign-in and access conditions, such as device, location, or risk context. It is not the best direct control for restricting fine-tuning permissions inside Foundry.
Expert reasoning
Use this exam shortcut:
Need to control access to enterprise data # think Purview access policies Need to restrict who can perform AI platform actions like fine-tuning # think RBAC in the AI platform So the correct mapping is:
Restricts access to the grounding data: Microsoft Purview access policies Restricts model fine-tuning: Role-based access control (RBAC) in Microsoft Foundry
NEW QUESTION # 97
What should you recommend to assist the CTO with the prebuilt agent selection process?
Answer: A
Explanation:
The CTO wants to view available prebuilt agent templates in Dynamics 365 Supply Chain Management to decide which one should be prioritized for deployment. Agent management is the feature used to discover, review, and manage available agent templates and capabilities for Dynamics 365 business applications.
Why this is correct:
It supports discovering available prebuilt agents
It helps evaluate which agent can deliver the most business value
It aligns with the requirement to preview and assess candidate agents during the selection phase Why the other options are not correct:
B). Immersive Home is more of an experience surface, not the primary tool for selecting prebuilt agent templates C). Lifecycle Services (LCS) is used for environment and application lifecycle management, not for browsing Dynamics 365 AI agent templates D). Copilot Studio is primarily for building/customizing copilots and agents, not for selecting Dynamics 365 prebuilt Supply Chain Management agent templates
NEW QUESTION # 98
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