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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Orchestrate multi-agent coordination	15-20%	- Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Configure multi-agent workflows to produce artifacts suitable for review and audit 2. Document key decisions, handoffs, and outcomes across agents 3. Perform post-hoc analysis of multi-agent behavior - 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 - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution - 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
Perform evaluation, error analysis, and tuning	15-20%	- Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Specify expected outcomes and operational constraints for agent tasks - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 3. Refine memory usage - 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
Manage memory, state, and execution	10-15%	- Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions - 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. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information
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 - 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 checked and approved 2. Validate agent plans 3. Configure an agent to output a structured plan 4. Configure agent planning to be distinct from agent execution
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. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 5. Scope permissions and execution contexts to enforce least-privilege access
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. Evaluate the execution context for an agent 4. Configure an agent's scope to a specific repository 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. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP allow lists 3. Configure the MCP registries 4. Configure a GitHub remote MCP server - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement rollbacks 5. Implement escalation paths

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Microsoft Developing in Agentic AI Systems Sample Questions (Q28-Q33):

NEW QUESTION # 28
Drag and Drop Question
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals. Each signal 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: The agent acknowledges the assignment and will create the draft pull request.
When an issue is assigned to the GitHub Copilot coding agent, the eyes emoji reaction indicates that the agent has acknowledged the task and is actively starting work in the background.
Box 2: The agent session is actively running and generating live logs.
The signal indicating that "the pull request timeline shows Copilot started work" means that the agent session is actively running and generating live logs.
When a pull request timeline shows that Copilot started work, it indicates that the execution context is actively working.
Actively working: Indicated when the pull request timeline explicitly logs that Copilot started work or updates the PR body with a list of in-progress sub-tasks.
Box 3: A human must manually approve and run the workflow.
When a draft pull request exists but GitHub Actions checks are not running, it indicates that the execution context is awaiting workflow approval.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/kick-off-a-task

NEW QUESTION # 29
You are evaluating the following workflow YAML.
jobs:
agent1:
runs-on: ubuntu-latest
environment: agent-sandbox
steps:
- uses: actions/checkout@v4
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:

NEW QUESTION # 30
You are evaluating the logs of the multi-agent workflow in repo2.
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:

NEW QUESTION # 31
You have a GitHub Actions workflow that runs GitHub Copilot-driven integration tests across multiple jobs.
You need to persist the agent memory/state so that it remains available across the jobs.
What should you do?

A. Use the runner's memory to retain the workspace between jobs.
B. Add the agent state directory to the repository and commit the changes at the end of each job.
C. Write the agent state to environment variables.
D. Upload the agent state as a workflow artifact and download the artifact in subsequent jobs.

Answer: D

Explanation:
Workflow artifacts provide an explicit mechanism for transferring serialized agent state between jobs. The producing job writes the required state to files and uploads those files as an artifact. Subsequent jobs download the artifact and load the state before continuing the integration-test workflow.
This is necessary because jobs do not automatically share process memory or a common workspace. They may execute on different runners, and even jobs assigned to similar runner types must not depend on incidental machine reuse. Uploading the state establishes a deliberate persistence boundary rather than relying on temporary execution infrastructure.
Committing state after every job introduces repository mutations merely to transport workflow data. Environment variables are also unsuitable as a general cross-job state store: setting a variable in one job does not automatically expose it in another, and complex state is better represented in structured files.
The application must serialize the information needed for continuation, such as completed steps, relevant decisions, and test progress. Uploading an arbitrary directory does not automatically restore a running agent process.
Relevant curriculum topics are durable state, cross-environment continuity, and resuming work without repeating completed steps.
Reference:

NEW QUESTION # 32
You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?

A. A prompt file (.prompt.md)
B. copilot-instructions.md
C. .copilotignore
D. agents.md

Answer: A

Explanation:
Prompt files define reusable, invocable prompt templates for specific tasks or personas. Unlike copilot-instructions.md, which applies globally and automatically, prompt files are opt-in and triggered explicitly.

NEW QUESTION # 33
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