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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Add clear, reproducible steps and expected behavior to the issue
B. Enable Copilot memory
C. Increase MCP server rate limits
D. Switch the agent to --allow-all mode

Answer: A

NEW QUESTION # 18
Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

You need to troubleshoot the issue reported by Ben.
What should you review?

A. the GitHub Actions runner log for the session job
B. the agent session log in the Agents panel
C. the GitHub Actions usage metrics of repo1
D. the GITHUB_TOKEN permissions block in the agent1 workflow

Answer: B

Explanation:
Scenario: A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes. Other developers report this intermittently as well.
To investigate a successful Copilot agent session that created an empty pull request, you should check the agent session log in the Agents panel.
Internal behavior: The empty pull request means the agent completed its logical execution loop without generating or committing code modifications.
Session context: The agent session log contains the specific LLM prompts, tool execution outputs, and file parsing steps that explain why the agent decided no changes were necessary.
Reference:
https://github.blog/ai-and-ml/github-copilot/whats-new-with-github-copilot-coding-agent/

NEW QUESTION # 19
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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:

NEW QUESTION # 20

Answer:

Explanation:

NEW QUESTION # 21
During a Copilot CLI session, an MCP tool call fails because the external service requires re- authentication. What is the most likely resolution path?

A. Switch to plan mode
B. Run /diff to inspect changes
C. Run /compact to clear context
D. Re-authenticate the MCP server connector

Answer: D

Explanation:
MCP tool failures due to expired or invalid credentials require re-authenticating the connector itself; conversation-management commands like /compact or /diff don't address authentication issues.

NEW QUESTION # 22
......

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