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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context
Topic 2: Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Scope permissions and execution contexts to enforce least-privilege access 3. Identify the subset of actions that require human judgment 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Block actions that violate defined security, compliance, or Responsible AI policies - 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 3: 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 - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access - 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 4: Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure a GitHub remote MCP server 3. Configure the MCP registries 4. Configure MCP allow lists - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Configure an agent to handle environment-specific constraints 3. Configure an agent's scope to a specific repository 4. Configure an agent to be invoked in a CI workflow 5. Evaluate the execution context for an agent 6. Configure an agent to use branch-based scope - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement rollbacks 5. Implement escalation paths
Topic 5: 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. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents - 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. 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 - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows
Topic 6: Prepare agent architecture and SDLC processes	15-20%	- Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Configure human intervention for autonomous agents without slowing delivery - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Validate agent plans 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checked and approved

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Microsoft Developing in Agentic AI Systems Sample Questions (Q40-Q45):

NEW QUESTION # 40
Drag and Drop Question
You have a GitHub repository that uses GitHub Actions for CI on pull requests.
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. Each value 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: npm test
npm test (or mvn test, pytest, dotnet test depending on your language stack).
The run keyword requires a shell command to execute your test runner.
Box 2: init
The CodeQL workflow must initialize its database and environment before building code. The official action for this is github/codeql-action/init.
Box 3: analyze
After the code is prepared (and built if using a compiled language), the final step scans the codebase and uploads the SARIF results to GitHub Security. The official action is github/codeql- action/analyze.
Reference:
https://gitprotect.io/blog/github-actions-how-to-use-it-for-security-and-compliance-needs/

NEW QUESTION # 41
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.
How should you configure the repository? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 42
Hotspot Question
You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
You need to enable parallel workflow executions across different pull request branches.
How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Set the concurrency at the workflow level
Isolates this concurrency rule to this specific workflow so it does not accidentally cancel other automation types.
Box 2: group:${{ github.head_ref || github.run_id}}
${{ github.head_ref }}: Evaluates to the source branch name during pull_request triggers, grouping all consecutive Copilot pushes to that specific branch together.
|| github.run_id: Serves as a fallback for non-PR triggers (like a direct push to main), ensuring the workflow still runs safely without canceling itself.
Reference:
https://www.meziantou.net/how-to-cancel-github-workflows-when-pushing-new-commits-on-a-branch.htm

NEW QUESTION # 43
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE.
Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?

A. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
B. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
C. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.
D. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention
@copilot in the pull request comments.

Answer: D

Explanation:
You should explicitly instruct the reviewer to mention @copilot in the pull request comments, and you must verify that they have write access to the repository.
Explicit Triggering: GitHub Copilot's asynchronous coding agent listens for active summons in existing pull request threads. Simply leaving a comment without an explicit @copilot mention may cause it to skip processing the update entirely, explaining why it fails to resume work.
Context Preservation: By directly mentioning @copilot within the specific pull request comment thread, the agent binds the new instructions directly to that exact active development session.
This prevents it from pulling outdated requirements from the initial issue definition.
Permission Enforcement: GitHub Copilot will ignore commands and mentions from users who do not possess write permissions (or higher) on the repository to prevent unauthorized code modifications or resource usage.
Reference:
https://github.blog/ai-and-ml/github-copilot/assigning-and-completing-issues-with-coding-agent-in-github-copilot/

NEW QUESTION # 44
You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
You discover that some workflow run artifacts are being deleted manually.
You need to use your organization's audit log data to identify which user deleted the artifacts.
Which audit log search filter should you use?

A. action:workflows.run
B. repo:<org>/<repo>
C. operation:remove
D. action:artifact.destroy

Answer: D

Explanation:
To identify which user manually deleted the workflow run artifacts, you should use the action:artifact.destroy search filter.When searching your GitHub Organization Audit Log, this filter targets the exact event triggered when a workflow run artifact is manually removed.
Reference:
https://docs.github.com/en/organizations/keeping-your-organization-secure/managing-security-settings-for-your-organization/reviewing-the-audit-log-for-your-organization

NEW QUESTION # 45
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