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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement error handling 3. Implement traceability and accountability for agent actions 4. Implement retries 5. Implement escalation paths - Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent's scope to a specific repository 4. Configure an agent to use branch-based scope 5. Evaluate the execution context for an agent 6. Configure an agent to handle environment-specific constraints - Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure a GitHub remote MCP server 3. Configure MCP allow lists 4. Configure MCP registries
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 - 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. Choose between short-term, long-term, and external memory 3. Define memory expiration, pruning, and reset rules
Prepare agent architecture and SDLC processes	15–20%	- Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Define inputs, outputs, and success criteria for agents 3. Identify and mitigate common anti-patterns in agents - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling - 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. Prevent agent action until the agent checks and approves 4. Validate agent plans
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Scope permissions and execution contexts to enforce least-privilege access 2. Block actions that violate defined security, compliance, or Responsible AI policies 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk 4. Identify the subset of actions that require human judgment 5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes - 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
Orchestrate multi-agent coordination	15–20%	- 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. 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 - 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 - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Generate evaluation signals by using automated scanning tools 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Specify expected outcomes and operational constraints for agent tasks 4. Align evaluation criteria with development intent - 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 - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints

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New GH-600 Sample Questions Answers | Reliable Microsoft GH-600 Test Questions: Developing in Agentic AI Systems

Whether you prefer web-based practice exam, desktop-based exam, or PDF real questions, we've got you covered. We believe that variety is key when it comes to Microsoft GH-600 Exam Preparation, and that's why we offer three formats that cater to different learning styles and preferences.

Microsoft Developing in Agentic AI Systems Sample Questions (Q85-Q90):

NEW QUESTION # 85
Hotspot Question
You have a GitHub repository that uses GitHub Actions for CI.
Your team is piloting the GitHub Copilot coding agent to autonomously create branches and open pull requests. The repository follows trunk-based development that uses main as the default branch.
You need to ensure that the agent meets the following requirements:
- Changes to main can occur only by using pull requests that have at
least one approval.
- When a pull request is opened, a validation workflow runs, and the
agent can still create branches and open pull requests autonomously.
How should you configure the repository? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Enabled
To meet the requirements, the Branch protection rule (main) - Require pull request before merging must be Enabled. This forces everyone-including GitHub Actions and AI agents-to create a branch and open a pull request first.
Box 2: on: pull_request
To allow your GitHub Copilot agent to autonomously open PRs while maintaining branch protection, you must trigger your validation workflow on the pull_request event.
Bypasses Protection: Agents cannot push directly to main due to your branch protection rule.
They must create a feature branch and open a PR.Triggers on Creation: The pull_request trigger fires automatically the moment the agent opens a PR.Runs in Isolation: It validates the merged result of the agent's branch and main before any actual code is merged.
Reference:
https://tutorialsdojo.com/github-copilot-coding-agent/

NEW QUESTION # 86
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 # 87
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. Use dependency caching to store trace.json and plan.md.
B. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
C. Commit trace.json and plan.md back to the repository from agent-run.
D. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.

Answer: B

Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run. Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence. A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference:

NEW QUESTION # 88
Drag and Drop Question
Your team uses a remote GitHub Model Context Protocol (MCP) server for workflows in the software development life cycle (SDLC).
You need to commit a workspace-scoped MCP configuration to ensure that GitHub Copilot can connect to the GitHub-hosted MCP endpoint and authenticate by using a GitHub personal access token (PAT).
How should you complete the mcp.json configuration file? 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: http
Type (http): The Model Context Protocol (MCP) configuration requires specifying the transport layer type. Since you are communicating via an API endpoint URL (https://...), the transport type is http (or in some implementations, standard variants like sse or streamable-http).
Box 2: Bearer
Authorization (Bearer ): GitHub's API endpoints authenticate Personal Access Tokens using the standard HTTP Bearer token schema. Note that a space is required between the word Bearer and the token variable itself.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

NEW QUESTION # 89
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated security scanning signals available for the pull request? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Dependency alerts that identify vulnerable package versions in the repository
B. The detection of hardcoded secrets, such as API keys and tokens
C. CodeQL findings that identify security vulnerabilities in the generated code
D. Linting errors from the repository's CI pipeline configured in GitHub Actions
E. The median time to merge metric reported on the Copilot usage metrics dashboard

Answer: B,C

Explanation:
CodeQL findings and secret-detection results are the intended selections. CodeQL analyzes code for patterns associated with vulnerabilities and programming errors. Secret scanning identifies sensitive material, including supported credential and token patterns, that may have been introduced into the generated changes.
Both provide concrete security evidence that a reviewer can evaluate alongside the proposed fix.
Linting generally evaluates coding conventions and selected correctness rules; a generic linting failure is not necessarily a security finding. Median time to merge measures delivery performance rather than the security of a particular change.
The wording of option D creates an important technical distinction. Dependency vulnerability detection is also a genuine automated security signal. GitHub explicitly documents dependency analysis alongside CodeQL and secret scanning in the coding agent's security checks. Repository-level dependency alerts, however, may concern existing vulnerabilities rather than changes introduced by this pull request. B and C are the clearest intended pair, but D cannot be dismissed as unrelated to security.
Reviewers should determine whether each finding applies to the proposed changes and whether the underlying vulnerability has actually been resolved.
Relevant curriculum topics are automated evaluation signals, security scanning, and evidence-based assessment of agent outputs.
Reference: Copilot coding agent security and quality validation .

NEW QUESTION # 90
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