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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Test, deploy, and monitor agentic AI systems	20%	- Deploy and monitor agents at scale 1. Deploy to Azure AI and cloud environments 2. Optimize cost, latency, and throughput 3. Implement logging, telemetry, and observability - Validate agent performance and safety 1. Evaluate quality metrics and iterate 2. Apply guardrails and content safety 3. Test reasoning accuracy and consistency
Topic 2: Implement agents and multi-agent systems	30%	- 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. Integrate models and prompts 2. Implement agent logic and reasoning 3. Develop using Semantic Kernel and Azure AI Foundry
Topic 3: Design agentic AI solutions	25%	- Design agent architecture 1. Plan tool integration and orchestration 2. Design memory and state management 3. Select agent patterns and topologies - Define requirements for agentic systems 1. Plan for responsible AI and governance 2. Define functional and non-functional requirements 3. Identify use cases and scenarios
Topic 4: Integrate tools, data, and services	25%	- Connect data sources and knowledge bases 1. Implement retrieval-augmented generation (RAG) 2. Integrate vector databases and search 3. Ensure data security and access control - Incorporate external tools and APIs 1. Design and register tool definitions 2. Implement function calling and service integration 3. Handle authentication and error resilience

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Microsoft Developing in Agentic AI Systems Sample Questions (Q83-Q88):

NEW QUESTION # 83
You are using GitHub Copilot Chat's agent mode in VS Code and want it to autonomously use tools (terminal, file edits, tests) to complete a multi-step task, checking in with you between major steps. What is this interaction mode called?

A. Ask mode
B. Inline mode
C. Agent mode
D. Edit mode

Answer: C

Explanation:
Agent mode allows Copilot Chat to autonomously plan and execute multi-step tasks using available tools, pausing for confirmation at key decision points rather than requiring manual step- by-step prompting.

NEW QUESTION # 84
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
Planning must be kept separate from executing.
Each plan must be captured without calling any tools.
Each webhook event must create a new independent session.
Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.

Answer:

Explanation:

NEW QUESTION # 85
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?

A. the artifact.destroy audit log event
B. the merge commit history
C. the pull request description and comment history
D. the checks.delete_logs audit log event

Answer: A

Explanation:
You should check the artifact.destroy audit log event.
This specific event tracks whenever a user or a token manually deletes a workflow run artifact. It provides exactly what you need to troubleshoot why your multi-agent review packages are disappearing.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization

NEW QUESTION # 86
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?

A. tools
B. handoffs
C. mcp-servers
D. target
E. agents

Answer: E

Explanation:
The agents frontmatter property defines the custom agents that the current agent can invoke as subagents. It establishes the delegation boundary for an orchestrator or other coordinating agent.
The tools property grants operational capabilities such as file access, terminal commands, MCP tools, or the agent invocation tool itself. It does not identify the specific custom-agent profiles available for delegation. The mcp-servers property configures external MCP servers; it does not define subagent membership.
Handoffs are user-facing transitions from one custom agent to another. They are useful for moving a conversation from planning to implementation but are not the configuration mechanism for an agent's available subagent array. The target field identifies an execution target or context and is unrelated to delegation membership.
For a reliable orchestration design, include only the required specialized agents in the agents list and ensure the parent agent has the appropriate agent-invocation capability.
Study-guide topics: subagent lists, delegation boundaries, orchestrator design, and multi-agent coordination.

NEW QUESTION # 87
You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A. From the Copilot CLI, switch to plan mode.
B. Start the Copilot CLI and specify the --allow-all parameter.
C. Start the Copilot CLI and specify the --agent=Task parameter.
D. From the Copilot CLI, run the /compact command.

Answer: A

Explanation:
The first action you should take is to switch to Agent mode or use the Plan agent to review, edit, and approve a detailed step-by-step Markdown plan before any code is modified.
Use the Plan Agent: If available in your environment, trigger the plan phase so Copilot scans your codebase in a read-only state to outline its entire approach first.
Review the Proposed Plan: Carefully inspect the resulting Markdown structure to catch any architectural issues or wrong directions before the "doing" phase begins.
Utilize edit Mode Intentionally: Alternatively, if you prefer granular control over file adjustments rather than a fully autonomous agent workflow, opt for edit mode. This allows you to specifically select the target files and describe the natural language updates manually.
Reference:
https://aidevme.com/think-before-you-build-github-copilots-plan-agent-in-visual-studio-structured-ai-assisted-development/

NEW QUESTION # 88
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