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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. Add an MCP server as a tool to an agent 2. Configure the MCP registries 3. Configure a GitHub remote MCP server 4. Configure MCP allow lists - 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 retries 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Evaluate the execution context for an agent 3. Configure an agent to use branch-based scope 4. Configure an agent to handle environment-specific constraints 5. Configure an agent's scope to a specific repository 6. Enable an agent to perform autonomous actions, including creating branches and pull requests
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. Block actions that violate defined security, compliance, or Responsible AI policies 3. Identify the subset of actions that require human judgment 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Scope permissions and execution contexts to enforce least-privilege access - 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: 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. Align evaluation criteria with development intent 4. Identify qualitative and quantitative evaluation signals to evaluate agents - 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 - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints
Topic 4: Orchestrate multi-agent coordination	15-20%	- 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. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Apply an orchestration pattern to coordinate multiple agents 3. Configure agent isolation for parallel execution - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Retire agents while preserving auditability and workflow continuity 3. Update, reconfigure, or replace agents without disrupting active workflows - 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
Topic 5: Manage memory, state, and execution	10-15%	- 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 - 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. Resume agent work without repeating steps or diverging from prior decisions 2. Capture task progress and decisions as durable artifacts 3. Detect and correct drift during extended agent execution
Topic 6: Prepare agent architecture and SDLC processes	15-20%	- Configure observability and control for autonomous agents 1. Configure human intervention for autonomous agents without slowing delivery 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Plan and implement the degree of agent autonomy, including guardrails - Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Configure agent planning to be distinct from agent execution 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

Microsoft Developing in Agentic AI Systems Sample Questions:

Question #1

You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Reveal Solution Discussion 0

Correct Answer:

Question #2

You have a GitHub repository that uses the GitHub Copilot coding agent.
Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
What should you do?

A. Store the token in a repository custom instructions file.
B. Add the token as a secret in the Copilot environment.
C. Add the token as an Actions repository secret.
D. Store the token in the agent configuration file.

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 in an ephemeral GitHub Actions environment.
Builds fail unless an environment variable named NPM_TOKEN is available, because the repository depends on a private package registry.
A workflow named .github/workflows/copilot-setup-steps.yml was added but was merged into a non-default branch and does NOT run when new agent sessions start.
You need to ensure that for every new agent session, the setup steps run, and the private registry token is available as NPM_TOKEN, without hardcoding secrets in the repository.
What should you do?

A. Disable the agent's integrated firewall and rename the workflow.
B. Move .github/workflows/copilot-setup-steps.yml to the repository's default branch and store the token as a GitHub Actions environment secret named NPM_TOKEN in the Copilot environment.
C. Create an organization-level Actions secret named NPM_TOKEN and reference it from .github/copilot-instructions.md.
D. Add the token directly under env: npm_token: in the workflow file.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #4

You need to enable Copilot memory to support the planned changes.
What should you configure?

A. The Copilot settings of the organization
B. The personal Copilot settings of each developer
C. The agent profile of agent1
D. The Copilot settings of each repository

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Question #5

You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?