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NEW QUESTION # 32
Case Study 2 - Contoso, Ltd
Overview
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 AI 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 AI solutions.
Requirements
AI Adoption
The following executives will have specific responsibilities in the overall AI adoption:
- Chief Technology Officer (CTO): Select one Dynamics 365 Finance,
Dynamics 365 Supply Chain Management or Dynamics 365 Commerce prebuilt
AI agent and one custom Microsoft Copilot Studio AI agent to prioritize and deploy during the initial AI adoption phase.
- Chief Information Officer (CIO): Ensure that appropriate security
labels are assigned to the data used by the AI agents.
- Chief Financial Officer (CFO): Analyze the return on investment (ROI) for the AI agents being deployed.
- Chief Information Security Officer (CISO): Discover and inventory AI
resources for auditing.
- Chief Executive Officer (CEO): Ensure that all solutions adhere to
industry-standard responsible AI practices.
All AI 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 AI 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 AI 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 AI 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 AI agent requirements:
- The custom AI agent will use data from Dynamics 365 Supply Chain
Management to answer questions for the manufacturing team as a low-code solution.
- The custom AI agent will be accessible from within Microsoft Teams.
- The custom AI agent must be designed to eventually connect to other
agents that can be selected based on their description.
- The topics used in the custom AI 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 AI 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 AI agent must be integrated with and accessible from
Dynamics 365 Supply Chain Management.
- The custom AI 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 analysis, reporting, and troubleshooting requirements:
- The CISO will audit all the AI solutions monthly for compliance and
security.
- The CFO will analyze all the AI 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 AI 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 AI 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 AI 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.
Hotspot Question
What should you include in the custom AI agent design to meet the R&D product specifications and the compliance information requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 33
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 modelExporting does not make it a governed grounding source.
B). Share WS1Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding.
C). Populate a Dataverse tableThe data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
NEW QUESTION # 34
A company has an Al business solution that uses Microsoft Copilot Studio agents. You need to recommend prompt best practices to improve the effectiveness of agent interactions. Which two actions should you include in the recommendation? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
For Microsoft Copilot Studio agents, effective prompting depends on giving the model strong guidance and continuously improving that guidance from real interactions.
C). Use clear and specific instructions in the prompts is correct because well-written prompts reduce ambiguity, improve intent interpretation, and make the agent's behavior more reliable and consistent.
D). Regularly test and refine the prompts based on user input is also correct because prompt quality improves through iteration. Reviewing how users actually interact with the agent helps identify unclear wording, missing instructions, and failure cases.
NEW QUESTION # 35
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? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
Explanation:
To implement a new Application Lifecycle Management (ALM) process for your fine-tuned Microsoft Foundry model, you can leverage Azure Blob Storage for raw data versioning and Azure AI Foundry (formerly Azure AI Studio) for managed data assets and model retraining.
1. Data Tracking and Versioning in Azure Blob Storage
Enable blob versioning to automatically maintain a history of your training data.
Enable Versioning: In the Azure portal, navigate to your storage account under Data management > Data protection and select Enable versioning for blobs.
Immutable Tracking: This ensures that every update to a training file creates a new version, allowing you to restore or reference specific historical states of your dataset for auditability.
2. Uploading and Managing Data in Microsoft Foundry
Integrate your versioned blob storage with Microsoft Foundry to create "Data assets" that the model can use for retraining.
Reference:
https://learn.microsoft.com/en-us/azure/storage/blobs/versioning-enable
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/data-add?view=foundry-classic
NEW QUESTION # 36
You need to recommend a security solution for agents in a Microsoft Power Platform environment.
The agents must use only approved connectors and services. The solution must prevent the agents from accessing sensitive data.
What should you recommend?
Answer: D
Explanation:
In a Power Platform environment, DLP policies act as the primary guardrail for controlling how data is shared across services and connectors. Here is how they address your specific requirements:
Approved Connectors: DLP policies allow administrators to categorize connectors into three groups: Business, Non-Business, and Blocked. By moving only "approved" connectors into the Business group and blocking others, you ensure agents can only use authorized tools.
Preventing Access to Sensitive Data: You can use DLP policies to prevent the mixing of data between groups. For example, if a "Business" connector (like SharePoint) and a "Non-Business" connector (like Twitter/X) are in different groups, the policy will prevent an agent from creating a flow that moves data from the sensitive business source to the public service.
Granular Control: Modern DLP policies also support Connector Action Control, allowing you to block specific actions (like "Delete") within an approved connector while allowing others.
NEW QUESTION # 37
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