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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement agents and multi-agent systems	30%	- 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 - Orchestrate multi-agent collaboration 1. Manage agent handoffs and task distribution 2. Implement workflows and coordination strategies 3. Define communication protocols between agents
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. Implement function calling and service integration 2. Handle authentication and error resilience 3. Design and register tool definitions
Design agentic AI solutions	25%	- Define requirements for agentic systems 1. Identify use cases and scenarios 2. Plan for responsible AI and governance 3. Define functional and non-functional requirements - Design agent architecture 1. Design memory and state management 2. Plan tool integration and orchestration 3. Select agent patterns and topologies
Test, deploy, and monitor agentic AI systems	20%	- Validate agent performance and safety 1. Evaluate quality metrics and iterate 2. Test reasoning accuracy and consistency 3. Apply guardrails and content safety - Deploy and monitor agents at scale 1. Implement logging, telemetry, and observability 2. Deploy to Azure AI and cloud environments 3. Optimize cost, latency, and throughput

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Microsoft Developing in Agentic AI Systems Sample Questions (Q39-Q44):

NEW QUESTION # 39
Your team wants Copilot's suggestions to reflect knowledge of internal library APIs that are not publicly documented and not present in the codebase being edited. What is the most appropriate solution?

A. Add the internal docs as copilot-instructions.md
B. Connect an MCP server that exposes the internal documentation
C. Enable plan mode
D. Use .copilotignore to hide public APIs

Answer: B

Explanation:
For external or large sets of documentation not resident in the repo, an MCP server can expose a searchable knowledge source Copilot can query dynamically, rather than trying to cram everything into static instruction files.

NEW QUESTION # 40
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 41
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.

A. action
B. user
C. agent_session_id
D. actor_is_agent

Answer: C,D

Explanation:
The agent_session_id field associates an activity with a particular agent session. It supports correlation of multiple events generated during the same agent-run lifecycle and helps investigators trace the origin of a change.
The actor_is_agent field identifies whether the event actor is an agent. This is the direct classification field for distinguishing agent-initiated activity from activity initiated by a human account or another type of actor.
The user field can identify an account associated with the event but does not independently establish that the action was performed by an agent. The action field identifies the recorded operation, such as a repository or workflow action, but does not itself identify the actor type.
Using both selected fields provides the most useful audit result: one confirms agent origin and the other supplies the session-level correlation needed to investigate the agent's activity sequence.
Study-guide topics: audit fields, agent attribution, session correlation, and accountability.

NEW QUESTION # 42
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 43
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?

A. a preToolUse hook
B. a Copilot firewall policy
C. a GitHub app installation policy
D. a postToolUse hook

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 # 44
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