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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20-25%	- Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement escalation paths 3. Implement retries 4. Implement traceability and accountability for agent actions 5. Implement rollbacks - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Configure MCP servers 1. Configure the 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 - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent's scope to a specific repository 3. Configure an agent to be invoked in a CI workflow 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to use branch-based scope 6. Configure an agent to handle environment-specific constraints
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. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Prepare agent architecture and SDLC processes	15-20%	- Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checked and approved - Configure observability and control for autonomous agents 1. Configure human intervention for autonomous agents without slowing delivery 2. Plan and implement the degree of agent autonomy, including guardrails 3. Configure agents to produce inspectable artifacts within standard development tooling - 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
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. Identify the subset of actions that require human judgment 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Scope permissions and execution contexts to enforce least-privilege access 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Respond to degraded behavior or coordination across agents - 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. 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 - 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
Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent stale context 3. Prevent conflicting context - 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 - 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 (Q54-Q59):

NEW QUESTION # 54
Drag and Drop Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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: Use the upload-artifact action and configure artifact retention in the CI workflow To meet this requirement you should use the actions/upload-artifact action and configure the retention-days property in your GitHub Actions CI workflow.
By default, GitHub retains workflow artifacts for a maximum of 90 days for public and private repositories (customizable down to 1 day). Utilizing these configurations ensures that human reviewers can download and inspect the agent's background outputs long after the initial execution concludes.
Box 2: Assign an issue and wait for the agent to view.
The best action is to assign an issue and wait for the agent to view.
Assigning a GitHub issue to the Copilot coding agent triggers it to autonomously start working on the background task. While investigating code and implementing the required fixes, it provides real-time, human-reviewable progress updates directly within the issue or pull request timeline (such as showing a "Copilot has started work" event, or tracking its steps inside the agent workflow). This completely satisfies the requirement for ongoing, transparent signaling for reviewers.
Box 3: Select View session to stream live agent logs
You should select "View session" (or navigate to the Agents tab) on GitHub to stream live agent logs.
When using the GitHub Copilot cloud agent asynchronously to resolve issues and generate draft pull requests, the process occurs entirely in a GitHub-hosted background environment. While automated status summaries eventually populate the pull request timeline, real-time auditing during an active run requires looking directly at the active session stream.
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/configuring-the-retention-period-for-github-actions-artifacts-and-logs-in-your-organization
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 55
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.

What is a possible cause of the error?

A. a postToolUse hook
B. a preToolUse hook
C. a Copilot firewall policy
D. a GitHub app installation policy

Answer: B

Explanation:
In GitHub Copilot Extensions and Agent frameworks (such as the GitHub Copilot Chat architecture), preToolUse hooks are explicitly designed to intercept, evaluate, and validate a tool's arguments before the agent executes the underlying command or function. Because the agent attempted to execute rm -rf /infra and was immediately blocked with a specific destructive_operation_detected reason, a preToolUse lifecycle hook intercepted the function call, identified the risk, and rejected the execution before the system could actually run the command.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents

NEW QUESTION # 56
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 # 57

Answer:

Explanation:

NEW QUESTION # 58
During a Copilot CLI session, an MCP tool call fails because the external service requires re- authentication. What is the most likely resolution path?

A. Run /diff to inspect changes
B. Re-authenticate the MCP server connector
C. Run /compact to clear context
D. Switch to plan mode

Answer: B

Explanation:
MCP tool failures due to expired or invalid credentials require re-authenticating the connector itself; conversation-management commands like /compact or /diff don't address authentication issues.

NEW QUESTION # 59
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