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NEW QUESTION # 87
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
Answer: B
NEW QUESTION # 88
A company extends Copilot in Microsoft Dynamics 365 Customer Service.
You need to recommend an automated application lifecycle management (ALM) process so that the Copilot components can be safely developed, tested, and promoted to production.
Which two actions should you include in the ALM process? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
To implement an automated Application Lifecycle Management (ALM) process for extending Microsoft Dynamics 365 Customer Service with Copilot, you should leverage Power Platform solutions and pipelines. This approach ensures that custom agents, knowledge sources, and connector actions are developed and promoted safely across environments.
Tools used include:
Power Platform Pipelines: Automates the deployment process across environments.
To automate the application lifecycle management (ALM) for Copilot components within Microsoft Dynamics 365 Customer Service, follow this structured process using Power Platform pipelines and solutions:
1. Solution-Centric Development
All Copilot components (agents, topics, custom actions, and connector plugins) must be developed within a Power Platform solution.
Create or Select a Solution: In Copilot Studio or the Power Apps maker portal, ensure you are working within an unmanaged solution in your Development environment.
Add Components: When you create new agents or actions in Microsoft Copilot Studio, they are automatically associated with the preferred solution you have set, ensuring they are portable.
2. Pipeline Configuration
Power Platform pipelines democratize ALM by providing a built-in CI/CD experience directly within the maker portal.
Reference:
https://intelequia.com/en/blog/post/maximize-the-value-of-power-platform-with-effective-alm-and- the-power-of-ai
https://learn.microsoft.com/en-us/power-platform/release-plan/2024wave2/microsoft-copilot- studio/solution-management-copilot-studio
NEW QUESTION # 89
Scenario: Your organization creates a new AI Center of Excellence (CoE) to guide enterprise- wide adoption of generative AI. A project team submits a proposal requesting immediate development of a generative AI model. They argue that identifying use cases and validating data quality can wait until after the prototype is built, since the CoE can "fix the data later." You are asked whether this approach aligns with Microsoft's recommended AI adoption lifecycle, which starts with identifying use cases, selecting domain-specific data, preparing and validating that data, designing and training solutions, and then monitoring and adapting them over time.
According to Microsoft's AI adoption guidance, is it appropriate to skip identifying use cases and validating domain-specific data before beginning AI model development? [Select Yes or No]
Answer: A
Explanation:
Microsoft's generative AI adoption framework - as shown in the diagram - emphasizes a sequenced lifecycle:
- Identify use cases
- Prepare, validate, and aggregate the required data
- Design, train, and validate AI solutions
- Monitor and adapt
The Microsoft Learn module clearly states that a Center of Excellence ensures organizations start with aligned business use cases and validated domain-specific data before any model development begins.
Skipping these early steps introduces high risk, creates misaligned solutions, and prevents effective contextualization of AI models.
Therefore, beginning model development without first identifying use cases and validating data does not follow Microsoft's recommended AI planning and adoption process.
References:
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/2-how-center-excellence-assists-planning-adoption-generative-ai
https://learn.microsoft.com/en-us/training/modules/intro-ai-center-excellence/1-introduction-generative-ai-center-excellence
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/center-of-excellence
NEW QUESTION # 90
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
Answer: A
Explanation:
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size
NEW QUESTION # 91
A company has a Microsoft 365 tenant in Canada and multiple Microsoft Power Platform environments in Canada and the United States. The company plans to deploy a Microsoft Copilot Studio agent to the Canadian environment that will use:
* Microsoft Dataverse data stored in Canada
* A connector that connects to an Azure OpenAI instance in the United States You need to ensure that the agent adheres to data residency and data movement policies before being deployed. What should you do?
Answer: D
Explanation:
The key issue is that the agent will run in a Canadian environment and use:
Dataverse data stored in Canada
a connector to Azure OpenAI in the United States
That means data may need to move across regions. Before deployment, the organization must make sure this cross-region use is explicitly allowed under the platform's data movement and residency controls.
That makes C the correct answer.
Why C is correct:
It directly addresses the fact that the solution uses services in different geographic regions It ensures the environment and its connector dependencies are configured to allow that movement in line with platform policy It is the most specific action tied to data residency and data movement compliance before deployment Why the other options are not correct:
A). Ensure that the data processed by Azure OpenAI is stored in the United States.This does not address whether the cross-region movement itself is permitted.
B). From the Microsoft Purview portal, validate the Data loss prevention settings.DLP helps govern connector usage and data exfiltration, but the question is specifically about data residency and cross-region data movement.
D). Migrate the tenant to the United States.This is unnecessary and does not align with the stated Canadian deployment requirement.
NEW QUESTION # 92
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