2026 GH-600: Developing in Agentic AI Systems–Reliable Exam Questions Fee

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement guardrails and accountability	10-15%	- 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 guardrails and human-in-the-loop workflows 1. Identify the subset of actions that require human judgment 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. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Perform evaluation, error analysis, and tuning	15-20%	- Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine tool usage and tool access 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. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools
Manage memory, state, and execution	10-15%	- 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 - 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. Detect and correct drift during extended agent execution 3. Capture task progress and decisions as durable artifacts
Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Evaluate the execution context for an agent 2. Configure an agent to handle environment-specific constraints 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to be invoked in a CI workflow 5. Configure an agent to use branch-based scope 6. Configure an agent's scope to a specific repository - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Operate agents with safe execution paths and robust error handling 1. Implement retries 2. Implement rollbacks 3. Implement escalation paths 4. Implement traceability and accountability for agent actions 5. Implement error handling - 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 the MCP registries
Orchestrate multi-agent coordination	15-20%	- Detect and respond to multi-agent failures and degraded behavior 1. Identify failed, partial, or stalled agent executions 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Respond to degraded behavior or coordination across agents - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Update, reconfigure, or replace agents without disrupting active workflows 3. Add agents to existing multi-agent workflows - 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. Perform post-hoc analysis of multi-agent behavior 2. Document key decisions, handoffs, and outcomes across agents 3. Configure multi-agent workflows to produce artifacts suitable for review and audit
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. Configure agent planning to be distinct from agent execution 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure an agent to output a structured plan - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q66-Q71):

NEW QUESTION # 66
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

A. Begins the implementation.
B. Opens a pull request that has the plan as a comment.
C. Creates a branch named /copilot/plan in the repository.
D. Saves the plan to plan.md.

Answer: D

Explanation:
In plan mode, Copilot creates a plan before implementation and saves that plan to plan.md. This makes the proposed approach available for review, refinement, and continuation rather than immediately executing repository changes.
The plan file creates a durable handoff point between planning and implementation. A developer can inspect the proposed work, assess dependencies and risk, adjust tasks, and decide whether implementation should proceed. This separation is essential for workflows that require approval before modification.
Plan mode does not automatically open a pull request or create a branch. It also does not begin implementation simply because a plan was generated. Those actions require a subsequent transition to an implementation workflow or a direct instruction from the user.
Study-guide topics: planning mode, durable plans, state persistence, and staged agent execution.

NEW QUESTION # 67
You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A. Start the Copilot CLI and specify the --agent=Task parameter.
B. From the Copilot CLI, run the /compact command.
C. Start the Copilot CLI and specify the --allow-all parameter.
D. From the Copilot CLI, switch to plan mode.

Answer: D

Explanation:
The first action you should take is to switch to Agent mode or use the Plan agent to review, edit, and approve a detailed step-by-step Markdown plan before any code is modified.
Use the Plan Agent: If available in your environment, trigger the plan phase so Copilot scans your codebase in a read-only state to outline its entire approach first.
Review the Proposed Plan: Carefully inspect the resulting Markdown structure to catch any architectural issues or wrong directions before the "doing" phase begins.
Utilize edit Mode Intentionally: Alternatively, if you prefer granular control over file adjustments rather than a fully autonomous agent workflow, opt for edit mode. This allows you to specifically select the target files and describe the natural language updates manually.
Reference:
https://aidevme.com/think-before-you-build-github-copilots-plan-agent-in-visual-studio-structured-ai-assisted-development/

NEW QUESTION # 68
You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 69
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?

A. MCP server integration
B. CODEOWNERS
C. .copilotignore
D. Tool/terminal invocation permissions in agent mode

Answer: D

Explanation:
Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.

NEW QUESTION # 70
You have a GitHub repository that uses GitHub Actions to run an autonomous agent job. The job summarizes a pull request and posts the summary as a pull request comment.
Your company stores required configuration values, such as JOB_NAME and RUNNER, as organization-level configuration variables and exposes them in workflows by using the vars context.
You discover that the agent job runs on push events and creates comments that are NOT tied to a pull request.
You need to ensure that the agent job runs only when the workflow run is triggered by a pull request. The solution must keep the job name and runner selection configured by using variables.
What should you do in the workflow?

A. Set jobs.agent.if: ${{ env.GITHUB_EVENT_NAME == 'pull_request' }}. Set jobs.agent.name and jobs.agent.runs-on by using the env context.
B. Set jobs.agent.if: ${{ github.event_name == 'pull_request' }}. Set jobs.agent.name and jobs.agent.runs-on by using the vars context.
C. Set jobs.agent.if: ${{ github.event_name == 'push' }}. Set jobs.agent.name and jobs.agent.runs-on by using the vars context.
D. Set jobs.agent.if: ${{ github.ref == 'refs/heads/main' }}. Set jobs.agent.name and jobs.agent.runs-on by using the vars context.

Answer: B

Explanation:
The job must evaluate the event that triggered the workflow. The expression github.event_name == 'pull_request' directly tests that condition, ensuring the agent job is skipped for push-triggered runs. GitHub's contexts documentation provides this exact job-level filtering pattern. Configuration variables remain accessible through vars, allowing the workflow to use name: ${{ vars.JOB_NAME }} and runs-on: ${{ vars.RUNNER }} without hard-coding either value.
Option A checks a branch reference rather than the triggering event. A push to the main branch could therefore satisfy the condition, which violates the requirement. Option B incorrectly substitutes the environment context for the organization's configuration-variable context. GitHub also distinguishes default environment variables from properties available through expression contexts. Option C explicitly selects push events and therefore preserves the unwanted behavior.
This configuration establishes the correct execution boundary before the autonomous job starts. It connects the agent's activity to the intended pull request event while retaining centrally managed configuration. The relevant curriculum topics are evaluating execution context and configuring agents to run within CI workflows.
Reference:

NEW QUESTION # 71
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