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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints - 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. Align evaluation criteria with development intent 2. Generate evaluation signals by using automated scanning tools 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Specify expected outcomes and operational constraints for agent tasks
Topic 2: 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Perform post-hoc analysis of multi-agent behavior 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 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. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity
Topic 3: Implement tool use and environment interaction	20–25%	- Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement retries 5. Implement rollbacks - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to use branch-based scope 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Evaluate the execution context for an agent 5. Configure an agent to handle environment-specific constraints 6. Configure an agent to be invoked in a CI workflow - Configure MCP servers 1. Configure MCP registries 2. Add an MCP server as a tool to an agent 3. Configure MCP allow lists 4. Configure a GitHub remote MCP server
Topic 4: Prepare agent architecture and SDLC processes	15–20%	- 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 - 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. Configure agent planning to be distinct from agent execution 4. Validate agent plans - 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
Topic 5: 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. 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. Identify the subset of actions that require human judgment - 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 6: Manage memory, state, and execution	10–15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Share agent state 3. Prevent stale context - 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. Scope agent memory to task-relevant information 2. Choose between short-term, long-term, and external memory 3. Define memory expiration, pruning, and reset rules

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Microsoft Developing in Agentic AI Systems Sample Questions (Q37-Q42):

NEW QUESTION # 37
Hotspot Question
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Start a new chat.
Clear the chat session or start a new conversation thread.
Context Reset: Erases the short-term conversation history completely.
Zero Overhead: Requires no configuration changes or administrative interventions.
Fresh State: Forces Copilot to focus only on newly provided code and prompts.
Box 2: Open related files and close unrelated files.
To keep GitHub Copilot Chat focused on the current work item with the minimum amount of effort, you should open related files and close unrelated files.
GitHub Copilot Chat automatically uses the open files and active tabs in your IDE as its immediate context. By closing unrelated files and keeping only relevant code files open, you instantly clean up the context window and force Copilot to focus solely on your current task without needing to modify complex settings.
Reference:
https://docs.github.com/en/copilot/how-tos/troubleshoot-copilot/troubleshoot-common-issues

NEW QUESTION # 38
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. 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.
B. Add the token directly under env: npm_token: in the workflow file.
C. Disable the agent's integrated firewall and rename the workflow.
D. Create an organization-level Actions secret named NPM_TOKEN and reference it from .github/copilot-instructions.md.

Answer: A

Explanation:
The Copilot setup workflow must be present on the repository's default branch to be used for new coding-agent sessions. Moving .github/workflows/copilot-setup-steps.yml there ensures the setup procedure is discoverable for every newly created agent environment.
The package-registry credential should be stored as an environment secret in the Copilot environment and exposed using the required variable name, NPM_TOKEN. This permits package installation without committing the token into workflow YAML, instructions, source code, or pull requests.
Disabling network controls does not resolve missing credentials and expands risk unnecessarily. Repository instructions can describe setup expectations but cannot safely substitute for an injected runtime secret. Adding the raw token to YAML permanently exposes it to repository history and anyone with repository access.
Study-guide topics: ephemeral environments, setup workflows, environment secrets, and secure dependency access.

NEW QUESTION # 39
You have a GitHub repository that uses the GitHub Copilot coding agent.
The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
What should you do?

A. Post a pull request comment that mentions @copilot.
B. Select Approve and run workflows in the pull request merge box.
C. Create a new issue and assign the issue to Copilot.
D. Close and reopen the issue.

Answer: A

Explanation:
A comment mentioning @copilot on the existing pull request provides the direct continuation mechanism. GitHub documents that this can start a new agent session and, by default, push additional commits to the pull request's branch. The comment should explicitly identify the latest repository guidance and the work that remains, correcting the context that produced the outdated proposal.
This approach preserves the existing branch, accumulated changes, review discussion, and relationship to the original issue. It also gives the agent a clear instruction tied to the artifact that must be updated. The commenter must have the required repository write access.
Creating another issue would establish a separate assignment and could duplicate work. Approve and run workflows authorizes GitHub Actions validation; it does not itself resume the coding agent's implementation session. Closing and reopening the issue is likewise not the direct pull-request continuation control.
The useful recovery action therefore combines a new execution trigger with an explicit statement of current intent. Restarting without correcting the outdated requirement could reproduce the same drift.
Relevant curriculum topics are resuming execution, restoring task context, and maintaining continuity without discarding prior work.
Reference:

NEW QUESTION # 40
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. Add Copilot as a bypass actor for ruleset1.
B. Make Copilot an owner of the repository.
C. Remove the Require signed commits rule from ruleset1.
D. Grant the Copilot coding agent permission to push directly to the copilot/ branches.

Answer: A

Explanation:
Adding Copilot as a bypass actor for the ruleset permits the coding agent to create and push its working changes without removing the signed-commit control for human developers. This is the narrowly scoped exception required by the scenario.
Making Copilot a repository owner grants excessive privilege and is not needed to resolve the signing restriction. Granting general push permission does not override a ruleset that blocks unsigned commits. Removing the signed-commit requirement weakens protection for every actor governed by the ruleset, including human developers on protected branches.
A bypass actor should be used deliberately and limited to the required automated identity and scope. The protected branch requirements, status checks, and human review processes should remain in place before agent-generated changes are merged.
Study-guide topics: rulesets, bypass actors, branch protection, and least-privilege exceptions.

NEW QUESTION # 41
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. /compact
B. /context
C. /diff
D. /usage

Answer: B

Explanation:
To verify your current token usage in the GitHub Copilot CLI, you should run the /context slash command.
When you execute /context during your interactive terminal session, it visualizes your context window breakdown. It helps you track token consumption by explicitly mapping out:
The active AI model being used.
The number of used tokens versus total capacity.
Token distribution among the System Prompt, System/MCP Tools, and active Message History.
Remaining Free Space and reserved response buffers.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli/overview

NEW QUESTION # 42
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