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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Orchestrate multi-agent coordination	15-20%	- Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior - Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution
Topic 2: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement escalation paths 5. Implement retries - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Evaluate the execution context for an agent 3. Configure an agent to be invoked in a CI workflow 4. Configure an agent to handle environment-specific constraints 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent to use branch-based scope - Configure MCP servers 1. Configure the MCP registries 2. Configure MCP allow lists 3. Configure a GitHub remote MCP server 4. Add an MCP server as a tool to an agent
Topic 3: Manage memory, state, and execution	10-15%	- Persist agent state and manage context drift 1. Detect and correct drift during extended agent execution 2. Resume agent work without repeating steps or diverging from prior decisions 3. Capture task progress and decisions as durable artifacts - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent stale context 3. Prevent conflicting context - Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Define memory expiration, pruning, and reset rules 3. Scope agent memory to task-relevant information
Topic 4: Prepare agent architecture and SDLC processes	15-20%	- Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents - Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Configure an agent to output a structured plan 3. Prevent agent action until the agent checked and approved 4. Validate agent plans - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling
Topic 5: Implement guardrails and accountability	10-15%	- Define autonomy levels 1. Classify agent actions by operational, security, and compliance risk to right-size human interventions 2. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards - Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Identify the subset of actions that require human judgment 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Scope permissions and execution contexts to enforce least-privilege access 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 6: Perform evaluation, error analysis, and tuning	15-20%	- Analyze agent failures and identify root causes 1. Classify root causes, including reasoning errors, tool misuse, and context or environment issues 2. Identify failures by using logs, plans, traces, outputs, and workflow artifacts - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access

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Microsoft Developing in Agentic AI Systems Sample Questions (Q87-Q92):

NEW QUESTION # 87
You are evaluating how agent1 will behave after you implement the planned changes.
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 # 88
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?

A. Modify the Chat > Agent: Max Requests setting.
B. Change from sidebar chat to inline chat.
C. Start a new Copilot Chat conversation.
D. Use @file to restrict the Copilot context to the currently open file.
E. Clear the chat history of the current conversation.

Answer: C

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 # 89
Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? To answer, drag the appropriate values to the correct targets. Each value 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.

Answer:

Explanation:

Explanation:
Box 1: http
Type (http): The Model Context Protocol (MCP) configuration requires specifying the transport layer type. Since you are communicating via an API endpoint URL (https://...), the transport type is http (or in some implementations, standard variants like sse or streamable-http).
Box 2: Bearer
Authorization (Bearer ): GitHub's API endpoints authenticate Personal Access Tokens using the standard HTTP Bearer token schema. Note that a space is required between the word Bearer and the token variable itself.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

NEW QUESTION # 90
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
- agent-run that generates trace.json and plan.md
- review that waits for human approval before continuing
- deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?

A. Use dependency caching to store trace.json and plan.md.
B. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
C. Commit trace.json and plan.md back to the repository from agent-run.
D. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.

Answer: B

Explanation:
1. Upload Artifacts (Job agent-run): Use the actions/upload-artifact action in your agent-run job to save trace.json and plan.md. This makes the files visible in the GitHub Actions UI and downloadable.
2. Configure Approval (Job review): Define an environment in your review job and configure Required Reviewers in your GitHub repository settings. The runner will pause until a human approves the run.
3. Download Artifacts (Job deploy): Use the actions/download-artifact action in the deploy job to retrieve the files uploaded by the agent-run job.
Reference:
https://docs.github.com/en/enterprise-server@3.20/admin/managing-github-actions-for-your-enterprise/getting-started-with-github-actions-for-your-enterprise/introducing-github-actions-to- your-enterprise

NEW QUESTION # 91
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 92
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