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Microsoft GH-600 Exam Syllabus Topics:

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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q107-Q112):

NEW QUESTION # 107
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. when the code that created the memory is deleted
B. when pull requests are merged
C. manually by repository members
D. automatically after 28 days
E. when the repository is archived

Answer: C,D

Explanation:
GitHub Copilot repository memories can be removed manually and through automatic retention cleanup. Repository owners or administrators can review repository-level facts under the repository's Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.
D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted. The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.
Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.
Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.

NEW QUESTION # 108
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. actor_is_agent
B. agent_session_id
C. user
D. action

Answer: A,B

Explanation:
The agent_session_id field associates an activity with a particular agent session. It supports correlation of multiple events generated during the same agent-run lifecycle and helps investigators trace the origin of a change.
The actor_is_agent field identifies whether the event actor is an agent. This is the direct classification field for distinguishing agent-initiated activity from activity initiated by a human account or another type of actor.
The user field can identify an account associated with the event but does not independently establish that the action was performed by an agent. The action field identifies the recorded operation, such as a repository or workflow action, but does not itself identify the actor type.
Using both selected fields provides the most useful audit result: one confirms agent origin and the other supplies the session-level correlation needed to investigate the agent's activity sequence.
Study-guide topics: audit fields, agent attribution, session correlation, and accountability.

NEW QUESTION # 109
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 Summarizer to the agents list.
B. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
C. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
D. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.

Answer: C

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 # 110
Drag and Drop Question
You have a GitHub repository that runs an agentic software development lifecycle (SDLC) workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel, and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors, and then fan in the results to a final executor.
How should you complete the workflow definition? 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: needs
Needs & [spec_analyzer, risk_reviewer] forces plan_merger to wait until both the analysis and risk review jobs have successfully executed in parallel, completing the fan-out portion of your pipeline.
Box 2: [spec_analyzer, risk_reviewer]
Box 3: concurrency
The top-level key prevents race conditions by grouping active workflow runs together. Using
${{ github.run_id }} (replacing the typo ${{ github.reg }}) correctly locks concurrent executions for the specific pipeline run.
Reference:
https://github.com/openclaw/openclaw/issues/38433

NEW QUESTION # 111
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 # 112
......

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