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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to use branch-based scope 3. Configure an agent to handle environment-specific constraints 4. Evaluate the execution context for an agent 5. Configure an agent to be invoked in a CI workflow 6. Enable an agent to perform autonomous actions, including creating branches and pull requests - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement escalation paths 3. Implement retries 4. Implement traceability and accountability for agent actions 5. Implement rollbacks - Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools - Configure MCP servers 1. Configure MCP allow lists 2. Add an MCP server as a tool to an agent 3. Configure the MCP registries 4. Configure a GitHub remote MCP server
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 - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Configure an agent to output a structured plan 3. Configure agent planning to be distinct from agent execution 4. Prevent agent action until the agent checked and approved - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Identify and mitigate common anti-patterns in agents 3. Define inputs, outputs, and success criteria for agents
Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context - Implement agent memory strategies 1. Choose between short-term, long-term, and external memory 2. Scope agent memory to task-relevant information 3. Define memory expiration, pruning, and reset rules - 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
Orchestrate multi-agent coordination	15-20%	- Operate and manage multi-agent workflows 1. Configure agent isolation for parallel execution 2. Apply an orchestration pattern to coordinate multiple agents 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - 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 - 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 - 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
Implement guardrails and accountability	10-15%	- Implement guardrails and human-in-the-loop workflows 1. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Scope permissions and execution contexts to enforce least-privilege access 3. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Identify the subset of actions that require human judgment - 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
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. 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. Specify expected outcomes and operational constraints for agent tasks 3. Generate evaluation signals by using automated scanning tools 4. Align evaluation criteria with development intent

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Microsoft Developing in Agentic AI Systems Sample Questions (Q49-Q54):

NEW QUESTION # 49
You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 50
You need to resolve the scoping issue associated to agent1.
What should you do?

A. Add a permissions block to the agent1 workflow in product-api.
B. Create a ruleset for billing-service and infra-terraform that blocks push access from the github-actions bot account.
C. Create a fine-grained personal access token (PAT) scoped to product-api and store the PAT as a GitHub Actions secret for agent1 to use.
D. To the profile of agent1, add a custom instruction specifying that the agent must NOT access billing-service or infra-terraform.

Answer: C

Explanation:
A fine-grained PAT restricted to product-api provides an enforceable repository-level authorization boundary for agent1. GitHub fine-grained personal access tokens can be limited to selected repositories and assigned only the repository permissions required by the workload. Consequently, even if agent1 attempts to access billing-service or infra-terraform, the credential does not authorize access to those repositories. GitHub specifically recommends fine-grained PATs when repository access must be restricted to particular repositories.
This directly aligns with the GH-600 objective to configure an agent's scope to a specific repository and define execution boundaries through repository context and permissions. Microsoft Learn defines execution context as including the repository an agent can access and emphasizes that repository scope is the first boundary limiting agent behavior.
Option A provides behavioral guidance but is not a security authorization boundary. Option B controls the permissions available to the workflow token but does not, by itself, establish the required cross-repository credential restriction. Option D only prevents certain push operations; it does not stop agent1 from reading or otherwise accessing the other repositories.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction → execution context and boundaries; repository-specific agent scope; tool permissions; least-privilege authentication.

NEW QUESTION # 51
Your organization wants to ensure the Copilot coding agent cannot push directly to main, and all its changes must go through a pull request review process like any other contributor. What enforces this?

A. .copilotignore
B. copilot-instructions.md
C. Standard branch protection rules on the main branch
D. /context

Answer: C

Explanation:
Branch protection rules apply uniformly regardless of whether the actor is a human or an agent; requiring pull requests and disallowing direct pushes to main ensures the coding agent's changes are always reviewed.

NEW QUESTION # 52
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. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
B. Commit trace.json and plan.md back to the repository from agent-run.
C. Use dependency caching to store trace.json and plan.md.
D. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.

Answer: D

NEW QUESTION # 53
You have a GitHub Enterprise Cloud organization that uses GitHub Actions for CI/CD.
You plan to enable an agent to update and deploy workflows that access production environment secrets.
You need to select an autonomy level for a compliance-sensitive workflow.
Which autonomy level should you select?

A. Read-only analysis with no ability to trigger workflows
B. Human-in-the-loop with required manual approvals for production
C. Fully autonomous execution without approvals
D. Autonomous execution limited to non-production environments

Answer: B

Explanation:
Human-in-the-loop execution provides the appropriate autonomy level because the agent's actions affect production deployment and access to production secrets. The agent can prepare changes and perform permitted validation, while an authorized person approves the production transition. This retains useful automation while placing human judgment at the consequential execution boundary.
GitHub Actions environments support required reviewers. A job referencing a protected environment must satisfy its protection rules before proceeding. GitHub explicitly documents that when approval is required, the job cannot access environment secrets until a required reviewer approves it. The approval gate therefore controls an operational capability, rather than merely asking the model to behave cautiously.
Option A removes the oversight required by the scenario. Option B prevents the agent from performing the requested deployment work. Option D restricts execution to non-production environments and consequently does not provide a controlled route to production.
The workflow should bind deployment to the protected production environment and align reviewer permissions with organizational policy. Relevant curriculum topics are risk-based autonomy levels, explicit authorization for compliance-sensitive changes, and least-privilege execution.
Reference:

NEW QUESTION # 54
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