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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - 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. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checks and approves 4. Configure an agent to output a structured plan
Topic 2: Orchestrate multi-agent coordination	15–20%	- Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity - Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Identify failed, partial, or stalled agent executions 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior - Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Apply an orchestration pattern to coordinate multiple agents
Topic 3: Implement guardrails and accountability	10–15%	- 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 - Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Identify the subset of actions that require human judgment 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Scope permissions and execution contexts to enforce least-privilege access
Topic 4: Implement tool use and environment interaction	20–25%	- 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 - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent to handle environment-specific constraints 4. Evaluate the execution context for an agent 5. Enable an agent to perform autonomous actions, including creating branches and pull requests 6. Configure an agent to use branch-based scope - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP registries 3. Configure MCP allow lists 4. Add an MCP server as a tool to an agent
Topic 5: Perform evaluation, error analysis, and tuning	15–20%	- 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 - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage - 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. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Topic 6: Manage memory, state, and execution	10–15%	- 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q18-Q23):

NEW QUESTION # 18
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:

NEW QUESTION # 19
Your company uses GitHub Copilot Enterprise.
Developers use GitHub Copilot agent mode in Microsoft Visual Studio Code on their laptops and Copilot Chat on github.com when they are away from their laptops.
When switching between environments, the developers notice that agent workflows lose continuity because the tools available in Visual Studio Code are unavailable on github.com.
You need to ensure that the agent tools and state are available consistently across environments and can be used from any device without local setup.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements.

Answer:

Explanation:

NEW QUESTION # 20
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?

A. The pull request description and comment history
B. The merge commit history
C. The artifact.destroy audit log event
D. The checks.delete_log audit log event

Answer: C

Explanation:
The artifact.destroy audit log event records manual deletion of workflow artifacts. It provides the event data needed to identify the actor responsible for the deletion and the time at which it occurred.
Pull-request comments and descriptions can describe review activity, but they are not authoritative evidence of artifact deletion. Merge commits only establish repository-history changes and do not record deletion of workflow artifacts. A checks-related log event is unrelated to artifact lifecycle management.
Audit records should be correlated with the affected repository, workflow run, artifact name, and timestamp. This produces a traceable explanation of why evidence from a particular run is no longer present. Where long-term retention is required, the organization should combine artifact policies with external audit-log retention or streaming.
Study-guide topics: artifact accountability, audit events, evidence retention, and workflow investigation.

NEW QUESTION # 21
You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?

A. Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
B. Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
C. Copilot stores memories indefinitely until a repository administrator deletes them manually.
D. Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.

Answer: B

Explanation:
GitHub Copilot Memory relies on a mechanism called just-in-time verification to ensure older or unmerged code changes do not pollute its code suggestions.
Reference:
https://docs.github.com/en/copilot/concepts/agents/copilot-memory

NEW QUESTION # 22
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
- agent-run that generates trace.json and plan.md
- review that waits for human approval before continuing
- deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?

A. Use dependency caching to store trace.json and plan.md.
B. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
C. Commit trace.json and plan.md back to the repository from agent-run.
D. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.

Answer: B

Explanation:
1. Upload Artifacts (Job agent-run): Use the actions/upload-artifact action in your agent-run job to save trace.json and plan.md. This makes the files visible in the GitHub Actions UI and downloadable.
2. Configure Approval (Job review): Define an environment in your review job and configure Required Reviewers in your GitHub repository settings. The runner will pause until a human approves the run.
3. Download Artifacts (Job deploy): Use the actions/download-artifact action in the deploy job to retrieve the files uploaded by the agent-run job.
Reference:
https://docs.github.com/en/enterprise-server@3.20/admin/managing-github-actions-for-your-enterprise/getting-started-with-github-actions-for-your-enterprise/introducing-github-actions-to- your-enterprise

NEW QUESTION # 23
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