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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 3. Prevent stale context - Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Scope agent memory to task-relevant information 3. Define memory expiration, pruning, and reset rules - 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
Topic 2: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Scope permissions and execution contexts to enforce least-privilege access 5. Identify the subset of actions that require human judgment - Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions
Topic 3: 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 steps for agents to perform 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 checks and approves 4. Validate agent plans - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Plan and implement the degree of agent autonomy, including guardrails 3. Configure human intervention for autonomous agents without slowing delivery
Topic 4: Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents - 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: Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Configure an agent to use branch-based scope 3. Evaluate the execution context for an agent 4. Configure an agent's scope to a specific repository 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent to handle environment-specific constraints - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement escalation paths 3. Implement rollbacks 4. Implement retries 5. Implement traceability and accountability for agent actions - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure 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 6: Orchestrate multi-agent coordination	15–20%	- Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Apply an orchestration pattern to coordinate multiple agents - Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Identify failed, partial, or stalled agent executions 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Configure multi-agent workflows to produce artifacts suitable for review and audit 2. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across agents

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Microsoft Developing in Agentic AI Systems Sample Questions (Q22-Q27):

NEW QUESTION # 22
You have a GitHub repository that uses the GitHub Copilot coding agent.
You need to complete the hooks configuration to meet the following requirements:
Tool usage must be evaluated before execution.
Build results must be logged after execution.
Tool usage must be logged after execution.
How should you complete the hooks configuration? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 23
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. when the repository is archived
B. when pull requests are merged
C. manually by repository members
D. when the code that created the memory is deleted
E. automatically after 28 days

Answer: C,E

Explanation:
GitHub Copilot repository memories can be removed manually and through automatic retention cleanup. Repository owners or administrators can review repository-level facts under the repository's Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.
D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted. The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.
Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.
Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.

NEW QUESTION # 24
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner. planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each statement, select Yes or No.

Answer:

Explanation:

NEW QUESTION # 25
You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

A. A CODEOWNERS file
B. A .copilotignore file
C. Branch protection rules
D. A repository ruleset

Answer: B

Explanation:
A .copilotignore file explicitly excludes specified files or directories from being read, indexed, or modified by Copilot, similar in syntax to .gitignore.

NEW QUESTION # 26
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. user
B. actor_is_agent
C. action
D. agent_session_id

Answer: B,D

NEW QUESTION # 27
......

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