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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20-25%	- Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement error handling 3. Implement traceability and accountability for agent actions 4. Implement retries 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Configure an agent to be invoked in a CI workflow 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to handle environment-specific constraints 5. Configure an agent's scope to a specific repository 6. Evaluate the execution context for an agent - Configure MCP servers 1. Configure MCP allow lists 2. Configure the MCP registries 3. Add an MCP server as a tool to an agent 4. Configure a GitHub remote MCP server
Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 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
Implement guardrails and accountability	10-15%	- 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 - Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Scope permissions and execution contexts to enforce least-privilege access 4. Identify the subset of actions that require human judgment 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
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 - 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 - 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
Prepare agent architecture and SDLC processes	15-20%	- 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 agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan - 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
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. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Generate evaluation signals by using automated scanning tools - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q87-Q92):

NEW QUESTION # 87
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf /, and curl ... | bash.
What should you do?

A. Configure a preToolUse hook that returns permissionDecision: "deny".
B. Configure a sessionStart hook that prints a policy banner.
C. Add .github/hooks/logs/ to .gitignore.
D. Configure a userPromptSubmitted hook that logs prompts and exits nonzero.

Answer: A

Explanation:
To safely restrict high-risk commands while allowing low-risk execution, you must configure a preToolUse hook that returns permissionDecision: "deny" when a restricted pattern is detected.
The preToolUse lifecycle hook executes immediately before GitHub Copilot CLI attempts to invoke any tool or execute a command. It evaluates the tool arguments and can programmatically intercept execution by writing a specific JSON payload to stdout. Returning
{"permissionDecision": "deny"} explicitly instructs Copilot CLI to block the autonomous execution of that command.
Reference:
https://code.visualstudio.com/docs/agent-customization/hooks

NEW QUESTION # 88
You have a GitHub repository that contains an agent named Orchestrator.Orchestrator delegates work to the following specialized subagents:
- Planner reviews issues and creates a plan of action.
- Implementer writes code based on the plan of action.
- Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.
C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.

Answer: D

Explanation:
To ensure that Orchestrator can invoke the new Summarizer agent as part of its workflow, you should take the following action: In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
In GitHub Copilot custom agent configurations (defined in .agent.md files), the properties specified in the YAML frontmatter serve distinct purposes:
agents: This field acts as an explicit whitelist of other custom subagents that the parent agent is allowed to invoke and delegate work to. Adding Summarizer here registers it as an authorized subagent within the Orchestrator workflow.
Reference:
https://awesome-copilot.github.com/learning-hub/building-custom-agents/

NEW QUESTION # 89
You have a GitHub repository that contains an agent named Orchestrator. Orchestrator delegates work to the following specialized subagents:
Planner reviews issues and creates a plan of action.
Implementer writes code based on the plan of action.
Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.
C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.

Answer: D

Explanation:
The configuration must be changed on the agent that performs the delegation. Orchestrator is responsible for coordinating the workflow, so its allowed subagent list must include Summarizer. Adding the new agent to that list makes it an available delegation target alongside Planner, Implementer, and Reviewer.
An agent's agents property identifies the custom agents it can invoke as subagents. The tools property serves a different purpose: it identifies available capabilities such as reading files, editing code, or invoking agents. Adding a custom agent's name to the tools list does not register that agent as a callable subagent. The agent invocation tool must also be available; the scenario already establishes that Orchestrator delegates to other subagents.
Changing Reviewer's configuration would enable a relationship originating from Reviewer, rather than the requested direct invocation by Orchestrator. A handoff also represents a different interaction pattern from having the orchestrating agent invoke a subagent and receive its result.
Availability does not force execution on every task. Orchestrator's instructions should indicate when a summary is required and what information it should contain.
Study-guide topics: delegation permissions, orchestrator responsibilities, and specialized subagents. Reference: VS Code-Subagents.

NEW QUESTION # 90
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:

NEW QUESTION # 91
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.

A. action
B. user
C. actor_is_agent
D. agent_session_id

Answer: C,D

NEW QUESTION # 92
......

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