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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage memory, state, and execution	10–15%	- 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. Scope agent memory to task-relevant information 3. Choose between short-term, long-term, and external memory - 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
Topic 2: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure MCP registries 2. Add an MCP server as a tool to an agent 3. Configure MCP allow lists 4. Configure a GitHub remote MCP server - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement traceability and accountability for agent actions 3. Implement error handling 4. Implement rollbacks 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent to use branch-based scope 6. Configure an agent to handle environment-specific constraints - Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools
Topic 3: 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. Plan and implement the degree of agent autonomy, including guardrails 3. Configure agents to produce inspectable artifacts within standard development tooling - 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 agent planning to be distinct from agent execution 2. Validate agent plans 3. Configure an agent to output a structured plan 4. Prevent agent action until the agent checks and approves
Topic 4: 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. Block actions that violate defined security, compliance, or Responsible AI policies 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Scope permissions and execution contexts to enforce least-privilege access 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Topic 5: 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. Align evaluation criteria with development intent 4. Generate evaluation signals by using automated scanning tools - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints - 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
Topic 6: Orchestrate multi-agent coordination	15–20%	- Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Apply an orchestration pattern to coordinate multiple agents - 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 - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Respond to degraded behavior or coordination across agents 3. Identify failed, partial, or stalled agent executions - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q15-Q20):

NEW QUESTION # 15
You need to resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?

A. Configure tools: ['read', 'search'] in both agent profiles to prevent either agent from writing files.
B. Configure a concurrency group on both agent workflows so that only one workflow runs at a time.
C. Add shared/config.yaml to a CODEOWNERS file that requires SG_Review approval before any changes can be merged.
D. Configure each agent to work on a separate branch and add a required status check that detects file-level overlap before either pull request can be merged.

Answer: D

Explanation:
Separate branches isolate each agent's proposed changes and allow the team to inspect them independently. Adding a required status check for file-level overlap detects conflicting modifications before either pull request is merged, preventing one agent's work from silently overwriting another's changes.
A read-only tool configuration prevents both agents from making required changes, so it avoids conflict by eliminating implementation capability rather than controlling it. CODEOWNERS approval adds review accountability for a specific file but does not automatically detect overlap between independent agent outputs. A single concurrency group serializes entire workflows and reduces throughput even where the agents work on unrelated files.
The selected solution maintains parallel development while creating a merge-time control for the actual risk: overlapping file changes. It also produces reviewable evidence of the overlap check in pull request status results.
Branch isolation does not eliminate semantic conflicts, such as incompatible API assumptions. Teams should therefore combine overlap detection with normal pull request review and integration testing.
Study-guide topics: parallel agent coordination, branch isolation, merge controls, and conflict detection.

NEW QUESTION # 16
A pull request created by the Copilot coding agent needs to be automatically closed if it fails to pass required checks after three retry attempts, per your organization's governance policy. What should you configure?

A. /compact
B. .copilotignore
C. agents.md
D. A repository ruleset with agent-specific merge conditions and CI enforcement

Answer: D

Explanation:
Repository rulesets allow organizations to define enforcement policies - including required status checks - that apply uniformly to pull requests, including those opened by autonomous agents, ensuring failing agent PRs cannot be merged.

NEW QUESTION # 17
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 # 18
You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?

A. Modify the .github/pull_request_template.md file.
B. Create a .github/copilot-instructions.md file.
C. Create a .github/instructions/copilot.instructions.md file.
D. Update the instructions in the .github/agents/*.agent.md files.

Answer: C

Explanation:
To apply repository-wide code review guidance that consistently impacts all pull request reviews, you must create a .github/copilot-instructions.md file in the root directory of your repository.
GitHub Copilot code review automatically ingests this file to use as a persistent checklist for every file change it analyzes.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/add-custom-instructions/add-repository-instructions

NEW QUESTION # 19
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?

A. Wait for the agent to complete.
B. Merge the draft pull request and mention @copilot on the merged pull request.
C. Unassign the issue from Copilot, and then reassign the issue to Copilot.
D. Select Approve and run workflows in the pull request merge box.

Answer: C

Explanation:
To restart the background session and force the agent to resume its task, unassign the issue from GitHub Copilot and then reassign it to Copilot.
This specific operational cycle terminates the frozen cloud background process and launches a fresh agent session. Because a draft pull request already exists and is bound to the issue context, the newly initiated session automatically detects the linked branch, picks up the previous implementation plan, and begins pushing new commits directly to that existing draft PR.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 20
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