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질문 # 97
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.
정답：
설명：
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
질문 # 98
A company uses a fine-tuned Microsoft Foundry model that requires frequent updates as new customer feedback becomes available.
You need to design an application lifecycle management (ALM) process that meets the following requirements:
* Data changes must be tracked and versioned.
* The model must be retrained consistently by using approved training data.
Which two actions should you include in the design?
NOTE: Each correct selection is worth one point.
정답：C,D
설명：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics Designing an ALM process for fine #tuned Microsoft Foundry models requires two critical capabilities:
* Version-controlled training data
* A consistent, governed pipeline for retraining
Let's break down the reasoning using modern Agentic AI lifecycle , data governance , and model retraining best practices .
E). Store the training data in Azure Blob Storage that has version control enabled - # Correct This directly satisfies the requirement:
"Data changes must be tracked and versioned."
Azure Blob Storage with versioning provides:
* Automatic version history for every training dataset
* Immutable snapshots for audit and rollback
* Governance controls for approved data
* Integration with CI/CD pipelines for model retraining
In an agentic AI lifecycle, data versioning is mandatory because:
* Training data evolves frequently
* Retraining must be reproducible
* Regulatory audits require traceability
* Model drift must be monitored
Blob Storage with versioning is the Microsoft#recommended approach for enterprise AI ALM.
D). Upload the training data to Microsoft Foundry data files - # Correct Foundry fine #tuning jobs require training data to be stored in Foundry data files .
This ensures:
* The fine #tuning job always uses the approved dataset
* The model retraining pipeline is consistent
* The data is validated and formatted correctly
* The training job references a stable, governed data source
This aligns with the requirement:
"The model must be retrained consistently by using approved training data." In agentic AI systems, the training pipeline must be deterministic.
Uploading the data to Foundry data files ensures that the fine#tuning job always uses the correct dataset version.
# Why the other options are NOT correct
A). Associate the storage location to the fine-tuning job - Not sufficient This does not provide:
* Data versioning
* Governance
* Tracking of changes
It simply points the job to a location, not a controlled ALM process.
B). Create a content filter - Not related to ALM or training data
Content filters are for safety , not:
* Versioning
* Data governance
* Retraining consistency
They do not help with the ALM requirements.
C). Store the training data in Azure Files - Not appropriate
Azure Files does not provide:
* Built#in versioning
* Immutable snapshots
* ALM integration for ML pipelines
Blob Storage is the correct choice for AI training data.
Final Answer: D, E
* D. Upload the training data to Microsoft Foundry data files
* E. Store the training data in Azure Blob Storage that has version control enabled These two actions together create a governed, versioned, repeatable ALM pipeline for fine #tuned Foundry models
질문 # 99
A manufacturing company wants to deploy an agent that will automate supplier invoice processing.
You are designing a solution to evaluate the financial implications of the deployment. The company is especially concerned about budget overruns.
You need to ensure that the solution considers the total cost of ownership (TCO), the expected savings from using automation, and whether to extend the existing AI capabilities.
What should you include in the design?
정답：A
설명：
This design plan outlines the financial and operational framework for deploying a Microsoft-based AI agent for supplier invoice processing. By leveraging current industry benchmarks and Microsoft's own agentic AI frameworks, this plan focuses on maximizing Return on AI Investment (ROAI) while mitigating the risk of budget overruns.
Reference:
https://www.investing.com/news/swot-analysis/microsofts-swot-analysis-cloud-and-ai-fuel-growth-but-challenges-loom-93CH-4197719
질문 # 100
A company uses Microsoft Dynamics 365 Finance to manage accounts payable.
You are designing an AI invoice processing solution.
You need to recommend the prerequisites to configure a prebuilt copilot for accounts payable.
What should you recommend?
정답：C
설명：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is D. From the Power Platform admin center, assign the Finance and Operations AI security role to users .
This question is asking for the prerequisite to configure a prebuilt copilot for accounts payable in Microsoft Dynamics 365 Finance . Since the copilot is already prebuilt , the requirement is not to create a new agent or build a custom AI tool. Instead, the needed prerequisite is proper access and security enablement for users.
Why D is correct
Prebuilt copilots in Dynamics 365 Finance and Operations apps rely on the platform's built-in configuration and security model. Before users can configure or use these AI capabilities, they must have the correct permissions. Assigning the Finance and Operations AI security role is the prerequisite that enables access to those AI experiences.
From a business solutions perspective, this makes sense because enterprise AI in finance functions must be governed carefully. Accounts payable touches:
* invoices
* vendors
* payment workflows
* financial controls
* audit-sensitive business data
Because of that, Microsoft requires the appropriate security role before users can configure or interact with the prebuilt copilot capabilities.
This is also aligned with responsible deployment practice: enable access through role-based controls first, then configure and use the copilot.
Why the other options are incorrect
A). From Microsoft Copilot Studio, create an accounts payable agent
This is incorrect because the question specifically says prebuilt copilot . A prebuilt copilot does not require building a new custom agent in Copilot Studio as a prerequisite.
B). Extend Microsoft 365 Copilot for Sales to an accounts payable agent This is unrelated. Microsoft 365 Copilot for Sales is focused on sales workflows, not accounts payable in Dynamics 365 Finance.
C). Build an AI tool in Microsoft Foundry
This is also unnecessary for a prebuilt copilot scenario. Foundry is for custom AI solution development, not the prerequisite step for enabling an out-of-the-box accounts payable copilot.
Expert reasoning
Use this exam pattern:
* If the question says prebuilt copilot , think enable/configure access , not build custom AI
* If the scenario is Dynamics 365 Finance / Finance and Operations , role-based setup is often the key prerequisite
* When the options include a specific AI security role , that is usually the required setup step
질문 # 101
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?
정답：D
설명：
The correct answer is A.
In Microsoft Foundry agent scenarios, MCP is used to let an agent discover and use external tools dynamically at runtime without requiring the agent itself to be modified each time a new tool or capability is added.
Why this is correct:
MCP standardizes how tools are exposed to agents
It allows the agent to connect to external capabilities in a flexible, runtime-driven way New tools can be made available through the MCP server without rebuilding or updating the core agent definition This directly matches the requirement: "ensure that an agent can dynamically use external tools at runtime without updating the agent."
질문 # 102
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