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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
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 - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Scope agent memory to task-relevant information 3. Choose between short-term, long-term, and external memory - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state
Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to handle environment-specific constraints 4. Configure an agent to use branch-based scope 5. Evaluate the execution context for an agent 6. Configure an agent to be invoked in a CI workflow - Configure MCP servers 1. Configure MCP registries 2. Configure MCP allow lists 3. Add an MCP server as a tool to an agent 4. Configure a GitHub remote MCP server - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement traceability and accountability for agent actions 3. Implement escalation paths 4. Implement error handling 5. Implement retries - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions
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. Identify the subset of actions that require human judgment 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Block actions that violate defined security, compliance, or Responsible AI policies 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
Orchestrate multi-agent coordination	15–20%	- Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Retire agents while preserving auditability and workflow continuity 3. Add agents to existing multi-agent workflows - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Perform post-hoc analysis of multi-agent behavior 3. Configure multi-agent workflows to produce artifacts suitable for review and audit - 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
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. Validate agent plans 4. Prevent agent action until the agent checks and approves - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents
Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints - Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Align evaluation criteria with development intent 4. Specify expected outcomes and operational constraints for agent tasks - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q36-Q41):

NEW QUESTION # 36
Hotspot Question
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
- Changes to main can occur only by using pull requests that have at
least one approval.
- When a pull request is opened, a validation workflow runs, and the
agent can still create branches and open pull requests autonomously.
How should you configure the repository? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Enabled
To meet the requirements, the Branch protection rule (main) - Require pull request before merging must be Enabled. This forces everyone-including GitHub Actions and AI agents-to create a branch and open a pull request first.
Box 2: on: pull_request
To allow your GitHub Copilot agent to autonomously open PRs while maintaining branch protection, you must trigger your validation workflow on the pull_request event.
Bypasses Protection: Agents cannot push directly to main due to your branch protection rule.
They must create a feature branch and open a PR.Triggers on Creation: The pull_request trigger fires automatically the moment the agent opens a PR.Runs in Isolation: It validates the merged result of the agent's branch and main before any actual code is merged.
Reference:
https://tutorialsdojo.com/github-copilot-coding-agent/

NEW QUESTION # 37
Your organization wants to ensure the Copilot coding agent cannot push directly to main, and all its changes must go through a pull request review process like any other contributor. What enforces this?

A. .copilotignore
B. Standard branch protection rules on the main branch
C. /context
D. copilot-instructions.md

Answer: B

Explanation:
Branch protection rules apply uniformly regardless of whether the actor is a human or an agent; requiring pull requests and disallowing direct pushes to main ensures the coding agent's changes are always reviewed.

NEW QUESTION # 38
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 # 39
Drag and Drop Question
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals. Each signal may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: The agent acknowledges the assignment and will create the draft pull request.
When an issue is assigned to the GitHub Copilot coding agent, the eyes emoji reaction indicates that the agent has acknowledged the task and is actively starting work in the background.
Box 2: The agent session is actively running and generating live logs.
The signal indicating that "the pull request timeline shows Copilot started work" means that the agent session is actively running and generating live logs.
When a pull request timeline shows that Copilot started work, it indicates that the execution context is actively working.
Actively working: Indicated when the pull request timeline explicitly logs that Copilot started work or updates the PR body with a list of in-progress sub-tasks.
Box 3: A human must manually approve and run the workflow.
When a draft pull request exists but GitHub Actions checks are not running, it indicates that the execution context is awaiting workflow approval.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/kick-off-a-task

NEW QUESTION # 40
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 Orchestrator agent, add Summarizer to the tools list.
B. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.

Answer: D

Explanation:
The configuration must be changed on the agent that performs the delegation. Orchestrator is responsible for coordinating the workflow, so its allowed subagent list must include Summarizer. Adding the new agent to that list makes it an available delegation target alongside Planner, Implementer, and Reviewer.
An agent's agents property identifies the custom agents it can invoke as subagents. The tools property serves a different purpose: it identifies available capabilities such as reading files, editing code, or invoking agents. Adding a custom agent's name to the tools list does not register that agent as a callable subagent. The agent invocation tool must also be available; the scenario already establishes that Orchestrator delegates to other subagents.
Changing Reviewer's configuration would enable a relationship originating from Reviewer, rather than the requested direct invocation by Orchestrator. A handoff also represents a different interaction pattern from having the orchestrating agent invoke a subagent and receive its result.
Availability does not force execution on every task. Orchestrator's instructions should indicate when a summary is required and what information it should contain.
Study-guide topics: delegation permissions, orchestrator responsibilities, and specialized subagents. Reference: VS Code-Subagents.

NEW QUESTION # 41
......

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