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NEW QUESTION # 11
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 AI-driven assistance to help staff organize and resolve work
orders.
- Deliver contextual AI 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,E
Explanation:
To enhance Microsoft Dynamics 365 Field Service with Microsoft Copilot for dispatchers and technicians, the following additional components and configurations are needed:
Essential Copilot Enabling Components
[E] Dynamics 365 Field Service License: Copilot for Field Service is included within the standard app, but must be enabled by an administrator in the Power Platform admin center.
[B] Copilot in Outlook & Teams: To enable dispatchers to turn emails into work orders and manage schedules directly in their flow of work, the Dynamics 365 Field Service for Outlook and Field Service app in Teams (preview) need to be configured.
New Field Service Mobile Experience: Technicians must have the latest mobile app (available in preview) enabled, which allows for natural language inputs, voice-to-text notes, and photo attachments.
Reference:
https://www.randgroup.com/microsoft/dynamics-365/copilot/
NEW QUESTION # 12
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 Al capabilities.
What should you include in the design?
Answer: B
Explanation:
The question asks for a design element that evaluates:
total cost of ownership (TCO)
expected savings from automation
whether to extend existing AI capabilities
Those are classic investment-evaluation considerations, so the best answer is B. a return on AI investment (ROAI) analysis.
Why B is correct:
ROAI analysis compares the financial benefits of the AI solution against its full costs It incorporates deployment cost, operating cost, maintenance, scaling, and savings from automation It is the right framework when the company is specifically worried about budget overruns and wants a business case for expansion or extension Why the other options are not sufficient:
A). adopting prebuilt agents to reduce deployment time may help cost indirectly, but it is not the financial evaluation framework being asked for C). a break-even analysis only is too narrow because the requirement explicitly includes TCO, savings, and expansion decisions D). training a custom model is an implementation choice, not the financial evaluation method
NEW QUESTION # 13
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 # 14
A company has a Microsoft Foundry generative Al model.
You need to evaluate the model ' s output to measure the overall quality and coherence of generated responses. The evaluation must use GPT-4o as a judge and return a numeric score for each output.
Which type of metric should you use?
Answer: D
NEW QUESTION # 15
A company has a Microsoft Power Platform solution that contains the following components:
* Microsoft Dataverse tables
* A Microsoft Power Bl workspace named WS1
* A canvas app named App1 that uses Dataverse
* A Power Bl semantic model that connects to Dataverse by using DirectQuery You plan to use generative Al to provide answers to queries based on a subset of corporate data. You need to ensure that the data is available as a grounding data source for Al systems. What should you do?
Answer: B
Explanation:
The goal is to use generative AI to answer questions based on a subset of corporate data , and to ensure that this data is available as a grounding data source .
The solution includes:
* Dataverse tables
* a Power BI workspace
* a canvas app
* a Power BI semantic model using DirectQuery to Dataverse
The best action is D. Endorse the semantic model.
Why D is correct:
* Endorsing a semantic model makes it a trusted, discoverable enterprise data asset
* It is the appropriate step when you want approved corporate data to be used reliably by downstream AI and analytics experiences
* It fits the requirement of exposing a curated subset of data as a grounding source rather than duplicating or manually exporting it Why the other options are not correct:
* A. Export the semantic model Exporting does not make it a governed grounding source.
* B. Share WS1 Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
* C. Populate a Dataverse table The data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
NEW QUESTION # 16
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