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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Agentic AI Solutions | 30-35% | - Configure AI agents using Microsoft Copilot Studio - Implement data connections and plugins - Set up agent-to-agent communication - Configure security and compliance settings |
| Topic 2: Deploy and Monitor Agentic AI Solutions | 20-25% | - Optimize agent performance - Publish and deploy agent solutions - Configure monitoring and analytics - Implement error handling and logging |
| Topic 3: Governance and Best Practices | 10-15% | - Ensure regulatory compliance - Apply responsible AI principles - Implement data privacy and security controls |
| Topic 4: Design Agentic AI Solutions | 30-35% | - Identify business scenarios for agentic AI - Design multi-agent architectures - Select appropriate AI models and services - Define agent capabilities and boundaries |
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質問 # 94
Drag and Drop Question
A company has a cloud-based Al solution that uses Azure OpenAI models.
You need to design a monitoring solution that meets the following requirements:
- Monitors performance metrics and operational health for the models
- Monitors Al apps and agents for compliance
- Uses Azure-native capabilities
- Minimizes development effort
What should you use for each requirement? To answer, drag the appropriate options to the correct requirements. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 95
A startup wants to build a customizable, agent-based workflow that can integrate with their internal APIs, retrieve contextual data from various sources, and run complex business logic autonomously. The team has moderate engineering skills but explicitly wants to avoid the overhead of managing underlying infrastructure. Which Microsoft AI service model best fits this requirement for building and deploying their agent-based solution?
正解：A
質問 # 96
A company plans to implement an AI solution that will contain a Microsoft Copilot Studio agent and a Microsoft Foundry agent. The solution will be stored in a source code repository.
You need to recommend a deployment method for each agent. The solution must meet the following requirements:
A test environment must be used before a deployment to production.
Production must be isolated from development and testing.
The deployment must be repeatable and fully automated.
The solution must NOT require manual intervention.
Which deployment method should you recommend for each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Copilot Studio # Use a Microsoft Power Platform deployment pipeline; Microsoft Foundry # Use an Azure DevOps pipeline
質問 # 97
Hotspot Question
A company plans to implement an AI solution that will contain a Microsoft Copilot Studio agent and a Microsoft Foundry agent. The solution will be stored in a source code repository.
You need to recommend a deployment method for each agent. The solution must meet the following requirements:
- A test environment must be used before a deployment to production.
- Production must be isolated from development and testing.
- The deployment must be repeatable and fully automated.
- The solution must NOT require manual intervention.
Which deployment method should you recommend for each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: Use a Microsoft Power Platform deployment pipeline
Copilot Studio agent
The preferred deployment method is to use a Microsoft Power Platform deployment pipeline.
Microsoft Power Platform pipelines are specifically designed to meet your requirements for a secure, automated, and repeatable application lifecycle management (ALM) process for Copilot Studio agents:
Box 2: Use a Bicep file
Microsoft Foundry agent
In the scenario described for a Microsoft Foundry agent, the preferred deployment method is to use a Bicep file.
This approach is best suited for your requirements because:
Infrastructure as Code (IaC): Azure Bicep allows you to define your entire environment-including the Foundry hub, projects, and model deployments-as code stored in your source repository.
Automation & Repeatability: Bicep files integrate directly with GitHub Actions or Azure Pipelines, enabling fully automated, repeatable deployments without manual intervention.
Environment Isolation: You can use Bicep to provision distinct, isolated resources for development, testing, and production by parameterizing the deployment for each environment.
Suitability: While Power Platform pipelines are used for Copilot Studio agents, Foundry-based agents are Azure resources where Bicep is the native and more powerful automation tool for managing the underlying infrastructure and model endpoints.
Reference:
https://learn.microsoft.com/en-us/power-platform/release-plan/2024wave2/microsoft-copilot- studio/solution-management-copilot-studio
https://techcommunity.microsoft.com/blog/azureinfrastructureblog/automating-azure-ai-foundry- deployment-with-iac-leveraging-bicep-and-github-work/4412155
質問 # 98
A company has an AI agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
正解：D
解説：
To automate feedback assessment and monthly reporting, you can build an agent flow in Microsoft Copilot Studio that functions as a "data pre-processor" and "insight generator." By using Agent Flows, you can move beyond simple Q&A to create structured, multi-step processes that clean raw data before it is ingested by the agent's main knowledge base.
Step 1: Design the Feedback Agent Flow
The "Flow" acts as the bridge between your cloud database (e.g., Azure SQL, Cosmos DB) and the final report.
Step 2: Ensure Data Quality (The "Clean Room")
For the agent to give accurate insights for marketing, the data must be grounded and structured.
Step 3: Automate the Monthly Report
Once the data is clean, the agent can summarize it into a report suitable for stakeholders.
Reference:
https://cps.co.uk/insights/how-to-use-copilot-studio-to-automate-repetitive-tasks
質問 # 99
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