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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
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. 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. Validate agent plans 2. Configure agent planning to be distinct from agent execution 3. Configure an agent to output a structured plan 4. Prevent agent action until the agent checks and approves - Integrate agents into the software development lifecycle (SDLC) 1. Define inputs, outputs, and success criteria for agents 2. Identify and mitigate common anti-patterns in agents 3. Identify steps for agents to perform
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. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across 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 - 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 - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Retire agents while preserving auditability and workflow continuity
Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine tool usage and tool access 2. Revise instructions, workflows, or constraints 3. Refine memory usage - 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. Generate evaluation signals by using automated scanning tools 3. Identify qualitative and quantitative evaluation signals to evaluate agents 4. Align evaluation criteria with development intent
Manage memory, state, and execution	10–15%	- 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 - Persist agent state and manage context drift 1. Resume agent work without repeating steps or diverging from prior decisions 2. Capture task progress and decisions as durable artifacts 3. Detect and correct drift during extended agent execution
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. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 3. Scope permissions and execution contexts to enforce least-privilege access 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 5. Identify the subset of actions that require human judgment - 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
Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement escalation paths 3. Implement traceability and accountability for agent actions 4. Implement error handling 5. Implement rollbacks - Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to use branch-based scope 4. Configure an agent to be invoked in a CI workflow 5. Configure an agent's scope to a specific repository 6. Configure an agent to handle environment-specific constraints - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP registries 3. Configure MCP allow lists 4. Configure a GitHub remote MCP server

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Microsoft Developing in Agentic AI Systems Sample Questions (Q90-Q95):

NEW QUESTION # 90
Your organization requires that any workflow file changes proposed by an autonomous agent be reviewed by a member of the security team before merging, regardless of who opened the pull request. What combination should you configure?

A. copilot-instructions.md + required status checks
B. CODEOWNERS + a required branch protection rule
C. MCP server permissions + repository ruleset
D. .copilotignore + agents.md

Answer: B

Explanation:
A CODEOWNERS file designates the security team as required reviewers for the .github/workflows/* path, and a branch protection rule enforces that their approval is required before any pull request touching that path can merge.

NEW QUESTION # 91
You have a GitHub repository that uses GitHub Copilot Chat in Microsoft Visual Studio Code. Custom agents are stored in the repository under version control.
Your team uses a multi-agent workflow where a planner agent produces an implementation plan that is then handed off to an implementation agent to make changes.
Recent prompts cause the planner agent to start editing files and running commands before the plan is approved.
You need to configure the planner agent to meet the following requirements:
Use only read-only tools.
Hand off to the implementation agent only after the plan is approved.
How should you configure the agent? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 92
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. manually by repository members
B. when pull requests are merged
C. automatically after 28 days
D. when the code that created the memory is deleted
E. when the repository is archived

Answer: A,C

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 # 93
You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?

A. a branch protection rule and copilot-instructions.md
B. a branch protection rule and a CODEOWNERS file
C. a ruleset and an agents.md file
D. a ruleset and a .copilotignore file

Answer: B

Explanation:
The correct solution combines a branch protection rule with a CODEOWNERS file. CODEOWNERS allows the repository to associate specific paths with designated users or teams. For example, entries can assign security or platform reviewers to .github/workflows/* and /infra/*. When a pull request modifies those paths, GitHub automatically identifies the corresponding code owners.
The enforcement mechanism comes from branch protection on main. Configure the protection rule to Require a pull request before merging and enable Require review from Code Owners. GitHub then blocks the merge until an applicable code owner approves the affected files. This converts CODEOWNERS from simple reviewer routing into an enforceable merge-control boundary.
agents.md and copilot-instructions.md provide behavioral guidance to AI agents; they do not enforce reviewer authorization. .copilotignore likewise does not establish mandatory merge approval. Although GitHub rulesets can also implement review controls, none of the ruleset choices provides the required CODEOWNERS pairing.
Study Guide Reference Topics: Implement Guardrails and Accountability; protected branches; required human review; CODEOWNERS; repository governance; approval gates.

NEW QUESTION # 94
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. Create a .github/copilot-instructions.md file.
B. Create a .github/instructions/copilot.instructions.md file.
C. Modify the .github/pull_request_template.md file.
D. Update the instructions in the .github/agents/*.agent.md files.

Answer: B

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 # 95
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