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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
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. Prevent agent action until the agent checked and approved 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Validate agent plans - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform
Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 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. 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
Perform evaluation, error analysis, and tuning	15-20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Generate evaluation signals by using automated scanning tools
Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions - Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Apply an orchestration pattern to coordinate multiple agents 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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. 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
Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement retries 3. Implement escalation paths 4. Implement error handling 5. Implement rollbacks - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent to handle environment-specific constraints 3. Configure an agent to be invoked in a CI workflow 4. Configure an agent's scope to a specific repository 5. Configure an agent to use branch-based scope 6. Enable an agent to perform autonomous actions, including creating branches and pull requests - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure the MCP registries 3. Configure MCP allow lists 4. Add an MCP server as a tool to an agent
Manage memory, state, and execution	10-15%	- Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q70-Q75):

NEW QUESTION # 70
You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
---
name: CodeAgent
description: Performs repository analysis and code review tasks.
tools: ['edit', 'execute', 'read', 'search']
---
You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
Which command should you run?

A. copilot agent run CodeAgent --allow-tool 'read,search' --deny-tool 'edit,execute'
B. copilot agent run CodeAgent --allow-tool 'read,search'
C. copilot agent run CodeAgent
D. copilot agent run CodeAgent --deny-tool 'edit,execute'
E. copilot agent run CodeAgent --allow-all-tools

Answer: A

Explanation:
To configure your GitHub Copilot agent (CA) to run low-risk, read-only tools without approval prompts while keeping high-risk tools strictly gated, you need to use the gh copilot CLI configuration command to set specific tool permissions.
gh copilot config set-agent-policy CA --allow-without-approval read search --require-approval edit execute Preserves Velocity: Bypasses confirmation prompts for read and search so code analysis runs instantly.
Maintains Security: Explicitly forces a manual user prompt before edit (modifying code) or execute (running commands) can run.
Targets the Agent: Uses the --allow-without-approval and --require-approval flags specifically mapped to the CA agent name declared in your YAML frontmatter.
Reference:
https://vivekfordevsecopsciso.medium.com/github-copilot-remote-code-execution-via-prompt-injection-cve-2025-53773-38b4792e70fb

NEW QUESTION # 71
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?

A. Run gh agent-task view and specify the --log and --jq parameters.
B. Run gh agent-task list and specify the --status and --jq parameters.
C. Run gh agent-task list and specify the --web parameter.
D. Run gh agent-task view and specify the --log and --follow parameters.

Answer: D

Explanation:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents

NEW QUESTION # 72
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 73
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

A. MCP server integration
B. .copilotignore
C. CODEOWNERS
D. Tool/terminal invocation permissions in agent mode

Answer: D

Explanation:
Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.

NEW QUESTION # 74
You want the GitHub Copilot coding agent to follow project-specific conventions (coding style, testing requirements, folder structure) on every task it performs in a repository. What should you create?

A. A CODEOWNERS file
B. A agents.yml workflow file
C. A .copilotignore file
D. A copilot-instructions.md file in .github/

Answer: D

Explanation:
Placing a copilot-instructions.md file inside the .github/ folder lets you define repository-wide custom instructions that Copilot automatically applies to every chat and agent session, ensuring consistent adherence to conventions without repeating them in every prompt.

NEW QUESTION # 75
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