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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent's scope to a specific repository 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to be invoked in a CI workflow 5. Configure an agent to use branch-based scope 6. Configure an agent to handle environment-specific constraints - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement traceability and accountability for agent actions 3. Implement rollbacks 4. Implement retries 5. Implement error handling - Configure MCP servers 1. Configure MCP allow lists 2. Configure MCP registries 3. Add an MCP server as a tool to an agent 4. Configure a GitHub remote MCP server - Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools
Topic 2: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Scope permissions and execution contexts to enforce least-privilege access 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Identify the subset of actions that require human judgment 5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes - 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
Topic 3: 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 - 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 - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information
Topic 4: Orchestrate multi-agent coordination	15–20%	- 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. 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 - 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 - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows
Topic 5: Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access - 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 - Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Topic 6: Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Prevent agent action until the agent checks and approves 2. Configure an agent to output a structured plan 3. Validate agent plans 4. Configure agent planning to be distinct from agent execution - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails

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Microsoft Developing in Agentic AI Systems Sample Questions (Q84-Q89):

NEW QUESTION # 84
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 pull requests are merged
B. when the code that created the memory is deleted
C. manually by repository members
D. automatically after 28 days
E. when the repository is archived

Answer: C,D

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 # 85
An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?

A. .copilotignore
B. agents.md
C. copilot-setup-steps.yml
D. copilot-instructions.md

Answer: C

Explanation:
copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.

NEW QUESTION # 86
You have a GitHub repository that contains an agent named Orchestrator.Orchestrator delegates work to the following specialized subagents:
- Planner reviews issues and creates a plan of action.
- Implementer writes code based on the plan of action.
- Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
B. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
C. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.

Answer: C

Explanation:
To ensure that Orchestrator can invoke the new Summarizer agent as part of its workflow, you should take the following action: In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
In GitHub Copilot custom agent configurations (defined in .agent.md files), the properties specified in the YAML frontmatter serve distinct purposes:
agents: This field acts as an explicit whitelist of other custom subagents that the parent agent is allowed to invoke and delegate work to. Adding Summarizer here registers it as an authorized subagent within the Orchestrator workflow.
Reference:
https://awesome-copilot.github.com/learning-hub/building-custom-agents/

NEW QUESTION # 87
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE.
Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?

A. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.
B. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
C. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
D. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention
@copilot in the pull request comments.

Answer: D

Explanation:
You should explicitly instruct the reviewer to mention @copilot in the pull request comments, and you must verify that they have write access to the repository.
Explicit Triggering: GitHub Copilot's asynchronous coding agent listens for active summons in existing pull request threads. Simply leaving a comment without an explicit @copilot mention may cause it to skip processing the update entirely, explaining why it fails to resume work.
Context Preservation: By directly mentioning @copilot within the specific pull request comment thread, the agent binds the new instructions directly to that exact active development session.
This prevents it from pulling outdated requirements from the initial issue definition.
Permission Enforcement: GitHub Copilot will ignore commands and mentions from users who do not possess write permissions (or higher) on the repository to prevent unauthorized code modifications or resource usage.
Reference:
https://github.blog/ai-and-ml/github-copilot/assigning-and-completing-issues-with-coding-agent-in-github-copilot/

NEW QUESTION # 88
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. Re-authenticate the MCP server connector
B. Switch to plan mode
C. Run /compact to clear context
D. Run /diff to inspect changes

Answer: A

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 # 89
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