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| Section | Weight | Objectives |
|---|---|---|
| Design Agentic AI Solutions | 30-35% | - Design multi-agent architectures - Identify business scenarios for agentic AI - Define agent capabilities and boundaries - Select appropriate AI models and services |
| Deploy and Monitor Agentic AI Solutions | 20-25% | - Implement error handling and logging - Publish and deploy agent solutions - Configure monitoring and analytics - Optimize agent performance |
| Governance and Best Practices | 10-15% | - Apply responsible AI principles - Ensure regulatory compliance - Implement data privacy and security controls |
| Implement Agentic AI Solutions | 30-35% | - Implement data connections and plugins - Configure AI agents using Microsoft Copilot Studio - Configure security and compliance settings - Set up agent-to-agent communication |
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問題 #23
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.
答案：
解題說明：
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
問題 #24
A company has Microsoft Power Platform development staging, and production environments. Each environment has its own Microsoft Dataverse tables and Azure Al Search index.
You are designing an application lifecycle management (ALM) process to deploy a Microsoft Copilot Studio agent between the environments.
The company has a Copilot Studio agent named Agent! in development. Agent1 uses the following grounding data sources:
* A Dataverse table named CustomerOrders
* An Azure Al Search index named customer-knowledge
You need to deploy Agent1 to production. The solution must ensure that the agent uses the production grounding data sources, minimizes downtime, and handles credentials and endpoints securely.
What should you include in the deployment package solution, and what should you reconfigure after the deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
In a proper ALM deployment for Microsoft Copilot Studio across development, staging, and production, you should package the agent in a way that is portable across environments while avoiding hardcoded endpoints, indexes, table targets, or credentials.
Here, Agent1 uses:
a Dataverse table: CustomerOrders
an Azure AI Search index: customer-knowledge
Because each environment has its own Dataverse tables and Azure AI Search index, the deployment package should not carry over the development environment's live connections as fixed production settings. Instead, it should carry the agent and the references needed so the target environment can bind to its own production resources.
That is why the correct recommendation is:
Deployment package: Agent1 and references to the data sources
After deployment: Reconfigure the environment variables
Why this is correct:
Environment variables are the standard ALM-friendly way to externalize settings like:
endpoints
index names
table references
connection-related values
This supports secure handling of credentials and endpoints
It also helps minimize downtime, because production values can be switched cleanly after import without rebuilding the agent Why the other choices are weaker:
Agent1 only would omit needed source references
The data sources only would not deploy the actual agent
Agent1 and the data source connections risks carrying environment-specific connection bindings Agent1, the data sources, and the data source connections is too tightly coupled to the source environment and is not the best ALM design for secure cross-environment deployment Reconfiguring only Dataverse or only Azure AI Search is incomplete because both can vary by environment Reconfiguring Agent1 configuration is broader and less precise than using environment variables
問題 #25
A company uses Microsoft Foundry agents.
You need to ensure that an agent can dynamically use external tools at runtime without updating the agent.
What should you include in the solution?
答案：C
解題說明：
In Microsoft Foundry, the Model Context Protocol (MCP) is the architectural standard used to decouple an agent's logic from its toolset, allowing it to discover and use external tools at runtime without requiring code updates or re-deployment.
Why an MCP Server is Required
Traditionally, integrating a tool required "hardcoding" its schema into the agent's definition. MCP replaces this with a dynamic discovery model:
Decoupling: The agent is only configured with the MCP server endpoint rather than individual tool definitions.
Runtime Discovery: When the agent starts a session, it queries the MCP server for a manifest of available tools, their parameters, and descriptions.
Zero-Update Scaling: You can add new capabilities to the MCP server (e.g., a new database query or API connection), and the agent will automatically "see" and use them in the next turn without any changes to the agent itself.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/model-context-protocol
問題 #26
Case Study 1 - Fabrikam, Inc
Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam's infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source - a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company's objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:
- Use low-code development to create a single AI agent that has
Dataverse as its core component.
- Ensure that sales managers can access unanswered correspondence from
prospects and intervene as appropriate.
- Replace the previous proprietary software with Dynamics 365 Sales to
track sales cycles and customer interactions.
- Have the sales executives use Dynamics 365 Sales to track
interactions for open opportunities and send follow-up communications
to prospects.
- Have the sales executives use handsfree headsets to interact with an
AI agent when they have questions about internal policies or customer
data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
- Azure must be used for all future infrastructure workloads.
- The company must follow Microsoft-recommended methodologies for
infrastructure migration to the cloud.
- Any created AI agents must have their return on investment (ROI)
calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
- The final AI agent must follow Microsoft recommendations for a
conversational user experience.
- A designated checklist must be reviewed to ensure that the AI agent
follows Microsoft deployment recommendations for a compliant solution.
- Detailed telemetry must be logged for the first created AI agent to
help troubleshoot and optimize the agent during the initial AI agent
adoption process.
- Unexpected AI agent actions must end in an escalation to a live
representative. For example, a sales executive must be rerouted to a
representative if the agent cannot answer a question after two failed
attempts.
- The return on investment (ROI) of switching from the current process
to the future process is required for stakeholder sign off.
- The sales team must use Dynamics 365 Sales to correspond with
prospects more quickly and efficiently than currently.
- Sales managers must report on the adoption of the AI agent to key
Fabrikam stakeholders on a monthly basis.
- Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
Hotspot Question
Which existing tool and data should you use to gather the required metrics for stakeholder signoff for the AI agents? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Scenario:
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off.
Box 1: Microsoft Foundry
Tool
To secure stakeholder sign-off for a Microsoft AI solution using AI agents and Dataverse, Azure AI Foundry (formerly Azure AI Studio) provides a structured framework to gather metrics across five key "ROI levers":
1. Identify Key ROI Metrics via Foundry
Use the Foundry ROI Framework to categorize and collect data that compares the current manual process to the future AI-driven state.
2. Gather Data using Foundry Tools
Foundry offers specific technical features to generate the evidence needed for your business case.
3. Calculate the ROI
Once metrics are gathered, apply the standard Microsoft-recommended formula for the business case.
Box 2: the current time to complete the task today per instance
Data required for the tool
In the Microsoft Foundry ROI framework, the metric for current time required to complete a task today is typically referred to as the Current Time per Instance or Task Duration (Baseline). This value serves as the primary "Before" benchmark to calculate the efficiency gains of the new AI agent solution.
Reference:
https://itnext.io/stop-wasting-tokens-how-to-design-high-roi-ai-apps-on-microsoft-foundry-
616622ddead6
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/a-framework-for-calculating-roi- for-agentic-ai-apps/4369169
問題 #27
You need to design a shared prompt library that will be used across multiple business units. The solution must meet the following requirements:
Ensure consistent AI responses with reusable formats.
Support governance and version control.
Minimize administrative effort.
Minimize ongoing costs.
What should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Ensure consistent AI responses # Define standardized prompt templates; Support governance and version control # Store prompts in a Git repository The requirement says the shared prompt library must ensure consistent AI responses with reusable formats.
The best way to achieve that is to create standardized prompt templates.
Standardized templates help multiple business units use the same structure for:
system instructions
response style
output formatting
safety guidance
business terminology
escalation behavior
This is important in AI-powered business solutions because prompt variability often leads to inconsistent outputs. A shared template approach creates repeatability and reduces drift across departments.
From an enterprise AI design perspective, standardized prompt templates improve:
response consistency
quality control
reusability
onboarding speed
compliance alignment
They also minimize administrative effort because teams do not need to invent new prompt structures from scratch.
Why "Store prompts in a Git repository" is correct
The requirement says the solution must support governance and version control while minimizing effort and cost.
A Git repository is the most appropriate answer because it provides:
version history
change tracking
rollback capability
collaboration support
approval workflows
auditability
This directly supports governance. It allows the organization to know:
who changed a prompt
what changed
when it changed
why it changed
That is extremely valuable for enterprise AI operations, especially when prompts influence business decisions, customer interactions, compliance behavior, or internal process automation.
Git also minimizes ongoing cost because it uses standard development and content management practices rather than requiring a separate expensive governance platform for prompt assets.
Why the other options are incorrect
Delegate department-specific prompt templates
This reduces consistency instead of improving it. Different departments creating their own templates introduces fragmentation, style drift, and governance challenges.
Maintain a prompt history
Keeping a history is useful, but it does not by itself ensure consistent reusable formats. It is more of a recordkeeping activity than a standardization mechanism.
Categorize prompts by business function
Categorization is helpful for organization and discoverability, but it does not provide version control or governance by itself.
Define standardized prompt templates for governance/version control
Standardization improves consistency, but version control specifically requires a system for tracking and managing changes. That is why Git is the better answer for the governance requirement.
Expert reasoning
A shared enterprise prompt library should usually follow this pattern:
Consistency and reuse # standard templates
Governance and versioning # Git-based repository
Low cost and low admin overhead # reuse existing tooling and centralized controls
問題 #28
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