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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q47-Q52):

NEW QUESTION # 47
Drag and Drop Question
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent.
You need to ensure that the default GITHUB_TOKEN permissions are read-only, and write access is granted to only the job that performs repository write operations. The workflow must be able to create and approve pull requests only when explicitly enabled.
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:
How should you complete the workflow?
Box 1: contents: read
Set Global Read-Only Permissions
To enforce the principle of least privilege, the top-level permissions block must strip all default write capabilities from the GITHUB_TOKEN. Setting contents: read allows the workflow to fetch the code but prevents any accidental repository modifications.
Box 2: contents: write
Grant Job-Level Write Access
Only the specific job responsible for modifying the repository should receive write access. The update_artifacts job requires permission to push code modifications back to the repository.
Box 3: pull-requests: write
To successfully create and approve pull requests, you must explicitly enable this capability in your GitHub repository settings:
Reference:
https://docs.github.com/en/organizations/managing-organization-settings/disabling-or-limiting-github-actions-for-your-organization

NEW QUESTION # 48
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?

A. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].
B. Replace "servers": with "mcp-servers": in mcp.json.
C. Replace line 04 with tools: ['copilot/*'].
D. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].

Answer: C

Explanation:
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.

NEW QUESTION # 49
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
* Planning must be kept separate from executing.
* Each plan must be captured without calling any tools.
* Each webhook event must create a new independent session.
* Once the plan is complete, tool calls must be enabled within the same event-specific session for follow- up.
How should you complete the Python code in the orchestrator.py file?

Answer:

Explanation:

Explanation:
Target 1 # " none "
Target 2 # [ " github " ]
The completed relevant code is:
session = await client.sessions.create(
purpose= " triage_and_fix " ,
session_id=event[ " delivery_id " ],
)
plan = await session.chat.completions.create(
messages=[
{
" role " : " system " ,
" content " : " Create an implementation plan only. Do not call tools. "
},
{
" role " : " user " ,
" content " : f " Webhook event:\n{event} "
},
],
tools=[ " github " ],
tool_choice= " none " ,
)
result = await session.chat.completions.create(
messages=[
{
" role " : " system " ,
" content " : " Execute the approved plan. Use tools as needed and log actions. "
},
{
" role " : " user " ,
" content " : plan.output_text
},
],
tools=[ " github " ],
tool_choice= " auto " ,
)
await session.close()
The planning request must set tool_choice= " none " . Although the GitHub tool is declared in the tools collection, tool_choice= " none " explicitly prevents the model from invoking any tool during that completion.
This ensures that the first phase produces only an implementation plan and cannot prematurely perform repository operations. By contrast, auto permits the model to decide whether to call an available tool. GitHub The execution request must expose [ " github " ] and retain tool_choice= " auto " . After the plan has been reviewed and accepted, the same event-specific session can therefore use GitHub capabilities as required to perform the approved work.
Session isolation is already implemented by session_id=event[ " delivery_id " ]. GitHub ' s Copilot SDK supports caller-defined session IDs, with each session maintaining its own conversation history, tool state, and planning context. Using the webhook delivery ID gives each delivery a distinct session identity, preventing unrelated webhook contexts from being combined while preserving continuity between the planning and execution phases of the same event. GitHub Docs
[ " playwright " ] is unrelated to the repository-operation requirement, while event and event[ " delivery_id " ] do not belong in either displayed blank.
Study Guide Reference Topics: Manage Memory, State, and Execution; session isolation; planning
/execution separation; tool gating; deterministic event-specific context; human-in-the-loop workflows.

NEW QUESTION # 50
You have a repository on github.com that uses the GitHub Copilot coding agent.
You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
You need to verify the current token usage.
Which Copilot CLI slash command should you run?

A. /compact
B. /usage
C. /context
D. /diff

Answer: C

Explanation:
The correct answer is /context when the requirement is to verify the current token usage of the active context window. GitHub documents /context as the command that displays the active model and the number of tokens currently in use relative to the model's total context capacity. It also provides a breakdown across the system prompt, custom instructions, built-in tools, MCP tools, conversation messages, free space, and reserved buffer.
This is distinct from /usage. /usage reports broader session statistics, including AI Credits consumed, session duration, lines edited, and per-model token totals. Therefore, /usage is appropriate when reviewing cumulative session consumption, but /context is the more precise answer to a question asking for the current token usage of the working context.
/compact reduces context pressure by summarizing earlier conversation history; it does not merely report the current token state. /diff reviews code changes and is unrelated to context or token accounting.
Study Guide Reference Topics: Manage Memory, State, and Execution; context-window monitoring; token-budget management; maintaining effective long-running agent sessions.

NEW QUESTION # 51
A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?

A. agents.md
B. copilot-setup-steps.yml
C. .copilotignore
D. copilot-instructions.md

Answer: A

Explanation:
agents.md (or AGENTS.md) is an emerging convention for providing agent-agnostic project context - build steps, structure, conventions -- that multiple AI coding tools (not just Copilot) can consume.

NEW QUESTION # 52
......

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