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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. Choose between short-term, long-term, and external memory 2. Define memory expiration, pruning, and reset rules 3. Scope agent memory to task-relevant information - 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
Topic 2: Orchestrate multi-agent coordination	15–20%	- Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Retire agents while preserving auditability and workflow continuity 3. Add agents to existing multi-agent workflows - 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 - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 3. Respond to degraded behavior or coordination across agents - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior
Topic 3: 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 4: Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP registries 4. Configure MCP allow lists - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement error handling 3. Implement retries 4. Implement traceability and accountability for agent actions 5. Implement rollbacks - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Evaluate the execution context for an agent 3. Configure an agent's scope to a specific repository 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to handle environment-specific constraints 6. Configure an agent to be invoked in a CI workflow
Topic 5: Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access - 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. Specify expected outcomes and operational constraints for agent tasks 2. Align evaluation criteria with development intent 3. Generate evaluation signals by using automated scanning tools 4. Identify qualitative and quantitative evaluation signals to evaluate agents
Topic 6: Prepare agent architecture and SDLC processes	15–20%	- Define boundaries between planning, reasoning, and action 1. Configure agent planning to be distinct from agent execution 2. Configure an agent to output a structured plan 3. Validate agent plans 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 - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q24-Q29):

NEW QUESTION # 24
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
The developers want to share a custom agent that can review code changes but cannot modify files.
You need to prevent the agent from modifying repository content and appearing in the agents dropdown.
How should you configure the YAML frontmatter for the agent? 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:

NEW QUESTION # 25
You have a GitHub Enterprise repository that uses the GitHub Copilot coding agent and opens draft pull requests for assigned issues.
Evaluation results show that the agent repeatedly opens pull requests that modify .github/workflows/*.yml to bypass failing checks instead of fixing the underlying code.
You need to ensure that the agent fixes the underlying code instead of bypassing the failing checks.
What should you do?

A. Update the .gitignore file.
B. Disable GitHub Actions workflows for pull requests created by the agent.
C. Start the GitHub Copilot CLI by using --allow-all.
D. Create a branch ruleset.

Answer: D

Explanation:
A properly configured branch ruleset establishes enforcement outside the agent's editable task instructions. It can require pull requests, successful checks, and appropriate reviews before changes enter protected branches. In an enterprise setup, required workflows can also be governed centrally so that changing a repository-local workflow does not remove the organization's validation requirement.
The ruleset must be configured to preserve the intended checks. Merely creating an empty ruleset does not solve the problem, and requiring a check whose implementation the agent can freely weaken is insufficient. Protecting workflow changes through required ownership review or centrally managed validation addresses that remaining gap. The agent should not receive bypass permission for these controls.
Option A increases authority rather than constraining the unwanted behavior. Option C removes the checks that are supposed to detect defective code. Option D affects treatment of untracked files and does not prevent changes to already tracked workflow definitions.
The control cannot guarantee successful reasoning, but it can prevent acceptance of a shortcut that defeats validation.
Relevant curriculum topics are blocking prohibited actions, enforcing independent controls, and correcting undesirable behavior identified through evaluation.
Reference:

NEW QUESTION # 26
You need Copilot's coding agent to interact with an external ticketing system (e.g., Jira) so it can pull issue details as part of its workflow. What should you configure?

A. A branch protection ruleset
B. A .copilotignore rule
C. A CODEOWNERS entry
D. An MCP server

Answer: D

Explanation:
Model Context Protocol (MCP) servers extend an agent's capabilities by connecting it to external tools and data sources, such as ticketing systems, databases, or internal APIs, in a standardized way.

NEW QUESTION # 27
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf, and curl ... | bash.
What should you do?

A. Configure a userPromptSubmitted hook that logs prompts and exits nonzero.
B. Configure a preToolUse hook that returns permissionDecision: "deny".
C. Configure a sessionStart hook that prints a policy banner.
D. Add .github/hooks/agent/ to .gitignore.

Answer: B

Explanation:
A preToolUse hook evaluates the proposed command before the shell tool executes it. The hook can inspect command arguments and return permissionDecision: "deny" when a pattern indicates a high-risk operation, while allowing low-risk commands to continue automatically.
This is the correct enforcement point because the requirement is preventive. A policy banner only informs the user or agent; it cannot block execution. Logging submitted prompts provides audit information but does not evaluate the actual shell command. Ignoring the hooks directory simply removes the repository-scoped enforcement mechanism.
The hook should use precise matching rules. For example, it can deny sudo, destructive recursive deletion, and piping untrusted remote content into a shell, while allowing commands such as git status, package metadata inspection, and test execution. Rules should avoid broad string matching that blocks harmless commands merely because they contain similar text.
Study-guide topics: pre-execution guardrails, shell-command approval, policy enforcement, and autonomous tool safety. Reference: GitHub Copilot-Hooks reference.

NEW QUESTION # 28
You have a GitHub repository that has a GitHub Actions workflow. The workflow runs an AI agent that deploys a containerized service.
You need to update the workflow so that only one production deployment can run at a time, and any currently running production deployments are cancelled when a new run starts.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 29
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