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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage memory, state, and execution	10–15%	- 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 - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Detect and correct drift during extended agent execution 3. Capture task progress and decisions as durable artifacts - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state
Topic 2: Implement tool use and environment interaction	20–25%	- Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Configure an agent to handle environment-specific constraints 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent's scope to a specific repository 5. Configure an agent to use branch-based scope 6. Evaluate the execution context for an agent - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement escalation paths 3. Implement rollbacks 4. Implement error handling 5. Implement traceability and accountability for agent actions - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Configure MCP registries 3. Add an MCP server as a tool to an agent 4. Configure MCP allow lists - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools
Topic 3: Orchestrate multi-agent coordination	15–20%	- 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 - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Retire agents while preserving auditability and workflow continuity 3. Update, reconfigure, or replace agents without disrupting active 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. Respond to degraded behavior or coordination across agents 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions
Topic 4: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Scope permissions and execution contexts to enforce least-privilege access - 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
Topic 5: Perform evaluation, error analysis, and tuning	15–20%	- 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. 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 - 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
Topic 6: 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 - Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Plan and implement the degree of agent autonomy, including guardrails 3. Configure human intervention for autonomous agents without slowing delivery - 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

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

NEW QUESTION # 37
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?

A. mcp-servers
B. agents
C. tools
D. target
E. handoffs

Answer: B

Explanation:
The agents frontmatter property defines the custom agents that the current agent can invoke as subagents. It establishes the delegation boundary for an orchestrator or other coordinating agent.
The tools property grants operational capabilities such as file access, terminal commands, MCP tools, or the agent invocation tool itself. It does not identify the specific custom-agent profiles available for delegation. The mcp-servers property configures external MCP servers; it does not define subagent membership.
Handoffs are user-facing transitions from one custom agent to another. They are useful for moving a conversation from planning to implementation but are not the configuration mechanism for an agent's available subagent array. The target field identifies an execution target or context and is unrelated to delegation membership.
For a reliable orchestration design, include only the required specialized agents in the agents list and ensure the parent agent has the appropriate agent-invocation capability.
Study-guide topics: subagent lists, delegation boundaries, orchestrator design, and multi-agent coordination.

NEW QUESTION # 38
Hotspot Question
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner.planner has tools set to ['search', 'read', 'fetch']. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation, and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs. Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: No
The configuration of the planner agent alone does not explicitly or fully prevent code changes from being produced automatically in the same conversation.
Box 2: No
No, removing the search tool will not resolve the issue, because the planner agent is not the one executing the network requests or writing the code.
The underlying problem is that the handoff configuration has an logic error (send: false), which prevents a clean, explicit agent migration. Because of this, the implementer agent takes over implicitly within the same conversation session, executing background code changes and attempting blocked internet calls.
Box 3: No
No, adding a .github/copilot-instructions.md file demanding tests and linters will not mitigate the stalled pull requests.
The root cause of your stalling pull requests is an underlying network connectivity issue, not a procedural omission by the agent.
Reference:
https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-custom-agents

NEW QUESTION # 39
Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? 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: http
Type (http): The Model Context Protocol (MCP) configuration requires specifying the transport layer type. Since you are communicating via an API endpoint URL (https://...), the transport type is http (or in some implementations, standard variants like sse or streamable-http).
Box 2: Bearer
Authorization (Bearer ): GitHub's API endpoints authenticate Personal Access Tokens using the standard HTTP Bearer token schema. Note that a space is required between the word Bearer and the token variable itself.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

NEW QUESTION # 40
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. Run /compact to clear context
C. Re-authenticate the MCP server connector
D. Switch to plan mode

Answer: C

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 # 41
You want the Copilot coding agent to scan a large codebase and propose a full, human- reviewable step-by-step plan before writing any code. What should you do first?

A. Run /compact
B. Enable --allow-all
C. Configure an MCP server
D. Switch to plan mode

Answer: D

Explanation:
Plan mode lets the agent operate in a read-only analysis state, producing a structured Markdown plan of intended changes. This allows you to catch architectural issues before the agent starts editing files.

NEW QUESTION # 42
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