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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20-25%	- Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP allow lists 3. Configure the MCP registries 4. Configure a GitHub remote MCP server - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Configure an agent's scope to a specific repository 3. Configure an agent to handle environment-specific constraints 4. Configure an agent to be invoked in a CI workflow 5. Evaluate the execution context for an agent 6. Enable an agent to perform autonomous actions, including creating branches and pull requests - 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 error handling 2. Implement retries 3. Implement rollbacks 4. Implement escalation paths 5. Implement traceability and accountability for agent actions
Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Scope permissions and execution contexts to enforce least-privilege access 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Identify the subset of actions that require human judgment 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk - 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
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 - 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. 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
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. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Specify expected outcomes and operational constraints for agent tasks - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage
Prepare agent architecture and SDLC processes	15-20%	- 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 - 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
Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Respond to degraded behavior or coordination across agents 3. Identify failed, partial, or stalled agent executions - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Add agents to existing multi-agent workflows - 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 - 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

Microsoft Developing in Agentic AI Systems Sample Questions:

Question #1

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

Reveal Solution Discussion 0

Correct Answer: A,B 🗳️

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Question #2

You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?

A. /diff
B. /context
C. /usage
D. /compact

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #3

You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository has a ruleset named ruleset1 that enforces the following:
Signed commits
Branch protections that require status checks to pass before merge
The agent is blocked from operating in the repository because it fails to comply with the signed-commits rule.
You need to ensure that the agent can create and push changes to copilot/ branches. The solution must enforce the signed-commits rule for human developers on protected branches.
What should you do?

A. Grant the Copilot coding agent permission to push directly to the copilot/ branches.
B. Make Copilot an owner of the repository.
C. Add Copilot as a bypass actor for ruleset1.
D. Remove the Require signed commits rule from ruleset1.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #4

You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
B. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
C. Mention @copilot in a pull request comment to request changes after the plan runs.
D. Add comments to the issues after assigning the issues to the agent.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Question #5

You have a GitHub Enterprise Cloud organization that uses GitHub Actions for CI/CD.
You plan to enable an agent to update and deploy workflows that access production environment secrets.
You need to select an autonomy level for a compliance-sensitive workflow.
Which autonomy level should you select?