Microsoft - GH-600 - Developing in Agentic AI Systems–Updated New Braindumps Free

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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Implement agents and multi-agent systems30%- Orchestrate multi-agent collaboration
  • 1. Define communication protocols between agents
    • 2. Implement workflows and coordination strategies
      • 3. Manage agent handoffs and task distribution
        - Build agents with Azure AI tools and frameworks
        • 1. Implement agent logic and reasoning
          • 2. Develop using Semantic Kernel and Azure AI Foundry
            • 3. Integrate models and prompts
              Test, deploy, and monitor agentic AI systems20%- Deploy and monitor agents at scale
              • 1. Optimize cost, latency, and throughput
                • 2. Deploy to Azure AI and cloud environments
                  • 3. Implement logging, telemetry, and observability
                    - Validate agent performance and safety
                    • 1. Test reasoning accuracy and consistency
                      • 2. Apply guardrails and content safety
                        • 3. Evaluate quality metrics and iterate
                          Integrate tools, data, and services25%- Incorporate external tools and APIs
                          • 1. Handle authentication and error resilience
                            • 2. Design and register tool definitions
                              • 3. Implement function calling and service integration
                                - Connect data sources and knowledge bases
                                • 1. Ensure data security and access control
                                  • 2. Integrate vector databases and search
                                    • 3. Implement retrieval-augmented generation (RAG)
                                      Design agentic AI solutions25%- Define requirements for agentic systems
                                      • 1. Define functional and non-functional requirements
                                        • 2. Identify use cases and scenarios
                                          • 3. Plan for responsible AI and governance
                                            - Design agent architecture
                                            • 1. Plan tool integration and orchestration
                                              • 2. Select agent patterns and topologies
                                                • 3. Design memory and state management

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                                                  New GH-600 Test Topics & Top GH-600 Questions

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                                                  Microsoft Developing in Agentic AI Systems Sample Questions (Q91-Q96):

                                                  NEW QUESTION # 91
                                                  After several long agentic sessions, you notice degraded response quality and want the agent to retain a concise summary of prior progress rather than the full raw history, while continuing without interruption. Which approach addresses this?

                                                  Answer: A

                                                  Explanation:
                                                  /compact summarizes the existing conversation history via the model and replaces the raw history with the condensed summary, preserving continuity and prior progress while freeing up context window space -- unlike /clear, which discards everything.


                                                  NEW QUESTION # 92
                                                  You need to implement agent2 to meet the technical requirements.
                                                  How should you complete the YAML configuration? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.
                                                  name: implementation-planner
                                                  description: Creates detailed implementation plans and technical specifications in markdown format tools: [
                                                  <Dropdown 1>,
                                                  <Dropdown 2>,
                                                  'microsoftdocs/mcp/docs_search',
                                                  'microsoftdocs/mcp/docs_fetch'
                                                  ]
                                                  The accompanying image includes empty dropdown controls and recreated practice alternatives.

                                                  Answer:

                                                  Explanation:

                                                  Topic 1, Contoso Ltd,
                                                  Overview
                                                  Contoso Ltd. is a software development company located in the United States.
                                                  Existing Environment
                                                  GitHub Environment
                                                  Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
                                                  Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1 that contains the following:
                                                  A custom agent named agent1 that includes instructions to review specs related to best practices A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
                                                  The front-end is stored in the /frontend folder.
                                                  The API logic is stored in the /api folder.
                                                  Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
                                                  Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
                                                  Problem Statements
                                                  The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
                                                  The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
                                                  Agent Logs
                                                  You have the following logs for the multi-agent workflow used in repo2.
                                                  Requirements
                                                  Planned Changes
                                                  Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
                                                  Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
                                                  Technical Requirements
                                                  App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
                                                  You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
                                                  All AI-generated code for UI styling must adhere to a predefined folder structure.
                                                  The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
                                                  The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.


                                                  NEW QUESTION # 93
                                                  After App1 is upgraded to meet the technical requirements, you need to validate the output.
                                                  For each of the following statements, select Yes if the statement is true. Otherwise, select No.
                                                  NOTE: Each correct selection is worth one point.

                                                  Answer:

                                                  Explanation:


                                                  NEW QUESTION # 94
                                                  In Microsoft Visual Studio Code, you are using GitHub Copilot Chat to generate documentation for a new feature.
                                                  Earlier in the day, you used Copilot Chat extensively for an unrelated refactoring task.
                                                  You discover that the Copilot responses for the new documentation task are influenced by the earlier conversation.
                                                  You need Copilot to focus only on the current task and avoid using prior conversational context. The solution must NOT affect other conversations.
                                                  What should you do?

                                                  Answer: B

                                                  Explanation:
                                                  Starting a new Copilot Chat conversation establishes a separate conversational context for the documentation task. The unrelated refactoring exchanges remain in their original conversation instead of continuing to influence the new thread. This directly addresses the requirement to separate tasks while preserving other conversations.
                                                  Conversation history contributes information beyond the latest prompt. Earlier instructions, assumptions, terminology, and design decisions can continue to affect subsequent responses within the same session. A fresh conversation provides a clear task boundary, allowing the developer to supply the feature requirements and relevant documentation context explicitly.
                                                  Changing from sidebar chat to inline chat changes the interaction surface; it does not provide the same explicit separation of conversational history. Referencing a file supplies relevant context but does not reliably remove earlier instructions. The maximum requests setting governs agent execution limits rather than conversation isolation. Clearing history is also unnecessary when the previous work can remain available in its own session.
                                                  A new conversation can still receive applicable repository instructions and deliberately supplied context; it is the previous conversation that is separated.
                                                  Study-guide topics: conversational state, context isolation, and task boundaries. Reference: VS Code-Manage agent sessions.


                                                  NEW QUESTION # 95
                                                  You have multiple GitHub Copilot coding agents that run tasks concurrently.
                                                  You live stream the session log output and see the following.

                                                  What is a possible cause of the error?

                                                  Answer: A

                                                  Explanation:
                                                  In GitHub Copilot Extensions and Agent frameworks (such as the GitHub Copilot Chat architecture), preToolUse hooks are explicitly designed to intercept, evaluate, and validate a tool's arguments before the agent executes the underlying command or function. Because the agent attempted to execute rm -rf /infra and was immediately blocked with a specific destructive_operation_detected reason, a preToolUse lifecycle hook intercepted the function call, identified the risk, and rejected the execution before the system could actually run the command.
                                                  Reference:
                                                  https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents


                                                  NEW QUESTION # 96
                                                  ......

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