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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement rollbacks 4. Implement error handling 5. Implement escalation paths - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent to be invoked in a CI workflow 3. Configure an agent's scope to a specific repository 4. Configure an agent to use branch-based scope 5. Configure an agent to handle environment-specific constraints 6. Enable an agent to perform autonomous actions, including creating branches and pull requests - Select and configure agent tools 1. Configure agent tool permissions 2. Configure agent tools 3. Identify required tools - Configure MCP servers 1. Configure MCP registries 2. Configure MCP allow lists 3. Configure a GitHub remote MCP server 4. Add an MCP server as a tool to an agent
Topic 2: Manage memory, state, and execution	10–15%	- Persist agent state and manage context drift 1. Detect and correct drift during extended agent execution 2. Capture task progress and decisions as durable artifacts 3. Resume agent work without repeating steps or diverging from prior decisions - 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 - Ensure continuity of agent memory and state across tools and environments 1. Share agent state 2. Prevent conflicting context 3. Prevent stale context
Topic 3: 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. Respond to degraded behavior or coordination across agents 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Apply an orchestration pattern to coordinate multiple agents 3. Configure agent isolation for parallel execution - 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
Topic 4: Prepare agent architecture and SDLC processes	15–20%	- Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Prevent agent action until the agent checks and approves 3. Configure an agent to output a structured plan 4. Configure agent planning to be distinct from agent execution
Topic 5: Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Identify the subset of actions that require human judgment 5. Scope permissions and execution contexts to enforce least-privilege access - 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
Topic 6: Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent - 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 memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints

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Microsoft Developing in Agentic AI Systems Sample Questions (Q79-Q84):

NEW QUESTION # 79
An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?

A. /clear
B. /usage
C. /reset-context
D. /compact

Answer: A

Explanation:
/clear wipes the entire conversation history and starts a fresh session. This differs from /compact, which summarizes history to save tokens while retaining prior progress.

NEW QUESTION # 80
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
- agent-run that generates trace.json and plan.md
- review that waits for human approval before continuing
- deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?

A. Commit trace.json and plan.md back to the repository from agent-run.
B. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
C. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
D. Use dependency caching to store trace.json and plan.md.

Answer: B

NEW QUESTION # 81
You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A. Start the Copilot CLI and specify the --allow-all parameter.
B. From the Copilot CLI, switch to plan mode.
C. From the Copilot CLI, run the /compact command.
D. Start the Copilot CLI and specify the --agent=Task parameter.

Answer: B

Explanation:
GitHub Copilot CLI plan mode is specifically designed for situations in which an implementation strategy must be developed and reviewed before code modifications begin. In plan mode, Copilot can inspect and analyze the repository, ask clarifying questions, and construct a structured implementation plan while protecting project files from normal editing operations. The resulting plan can then be reviewed and approved before Copilot proceeds with implementation. GitHub explicitly describes plan mode as a mechanism for identifying misunderstandings before code is written and maintaining human control over complex, multi-step work.
Option A is incorrect because --agent=AGENT selects a custom agent; it does not establish a planning-and-approval phase. Option B is the opposite of the requirement: --allow-all grants Copilot broad permission to use tools, paths, and URLs without individual approval. Option D is also incorrect because /compact relates to managing conversation/context size rather than establishing an implementation plan.
Therefore, switching to plan mode is the appropriate first action when the development process requires human review and agreement before changes are applied.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; human-in-the-loop development; planning before implementation; controlled agent execution.

NEW QUESTION # 82
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 83
You need to troubleshoot the issue reported by Dev1.
What should you review?

A. The GitHub Actions runner log for the session job.
B. The GitHub Actions usage metrics of repo1.
C. The agent session log in the Agents panel.
D. The GITHUB_TOKEN permissions block in the agent1 workflow.

Answer: C

Explanation:
The agent session log is the appropriate starting point when the reported problem concerns an agent's interaction, execution sequence, or tool activity within the development environment. It provides information closer to the failing behavior than repository-level workflow usage statistics.
A useful investigation establishes what the agent was asked to do, which operations it attempted, what responses those operations returned, and where progress stopped or diverged from the intended result. Session diagnostics can help distinguish an instruction problem from an unavailable tool, a rejected operation, or an execution error. VS Code also provides detailed chat debugging facilities for inspecting interactions and diagnosing failures.
GitHub Actions usage metrics primarily describe workflow consumption. Runner logs are relevant when evidence points to a hosted workflow or runner problem. The token permissions block becomes relevant when the failure concerns authorization to a GitHub resource. None should be assumed to be the root cause before examining the reported execution.
The PDF selects B; the underlying Dev1 incident description is not included, so this selection assumes a session-level agent issue.
Study-guide topics: diagnostic evidence, tool-call inspection, and failure localization. Reference: VS Code-Debug chat interactions.

NEW QUESTION # 84
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