Updated Certification GH-600 Exam Infor & Leader in Qualification Exams & Newest GH-600: Developing in Agentic AI Systems

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
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 - 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. Apply an orchestration pattern to coordinate multiple agents 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 3. Configure agent isolation for parallel execution - 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
Implement tool use and environment interaction	20–25%	- Operate agents with safe execution paths and robust error handling 1. Implement rollbacks 2. Implement retries 3. Implement escalation paths 4. Implement traceability and accountability for agent actions 5. Implement error handling - Configure MCP servers 1. Configure MCP allow lists 2. Configure a GitHub remote MCP server 3. Add an MCP server as a tool to an agent 4. Configure MCP registries - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Integrate agents within development environments 1. Enable an agent to perform autonomous actions, including creating branches and pull requests 2. Configure an agent to use branch-based scope 3. Configure an agent to be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent's scope to a specific repository 6. Configure an agent to handle environment-specific constraints
Perform evaluation, error analysis, and tuning	15–20%	- Tune agent behavior based on evaluation results 1. Refine memory usage 2. Revise instructions, workflows, or constraints 3. Refine tool usage and tool access - Define success criteria and evaluation signals for agent tasks 1. Align evaluation criteria with development intent 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools - 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
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. Identify the subset of actions that require human judgment 4. Preserve execution velocity by minimizing approvals that do not materially reduce risk 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
Prepare agent architecture and SDLC processes	15–20%	- Integrate agents into the software development lifecycle (SDLC) 1. Define inputs, outputs, and success criteria for agents 2. Identify steps for agents to perform 3. Identify and mitigate common anti-patterns in agents - Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Configure agent planning to be distinct from agent execution 3. Validate agent plans 4. Prevent agent action until the agent checks and approves - Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Configure human intervention for autonomous agents without slowing delivery
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 - 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

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Microsoft Developing in Agentic AI Systems Sample Questions (Q64-Q69):

NEW QUESTION # 64
A team assigns an issue to the GitHub Copilot coding agent by using the following one-line description: Fix the login bug.
Copilot creates a pull request, but the pull request is missing changes and has an incorrect scope.
How should you resolve the issue?

A. Add a clear description of the problem to the issue.
B. Allocate more resources to copilot-setup-steps.yml.
C. Enable Copilot memory.
D. Update the Model Context Protocol (MCP) server rate limits.

Answer: A

Explanation:
Adding a clear description of the problem to the issue will directly remedy this problem.Coding agents like GitHub Copilot rely heavily on the context, details, and constraints provided in the issue to understand what needs to be fixed. A one-line description like "Fix the login bug" is too vague, leading to guesswork, incorrect scope, and incomplete code changes.
Defines Scope: Explicitly stating what is broken prevents the agent from changing unrelated files.
Identifies the Root Cause: Providing error logs or steps to reproduce guides the agent to the exact lines of code that need fixing.
Sets Expectations: Specifying the expected correct behavior ensures the agent generates all the necessary changes, preventing missing code.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 65
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

NEW QUESTION # 66
Hotspot Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests.
You assign an issue to Copilot. Copilot creates a draft pull request. The pull request timeline shows Copilot started work, followed by status updates. The most recent status update is 55 minutes old.
You discover that the agent is no longer making progress.
You need to ensure that the work resumes without redoing the completed steps or changing the previously chosen approach.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Select View session from the pull request
To best confirm the agent's progress, troubleshoot the stagnation, and ensure the work resumes seamlessly from where it left off, you should interact directly with the agent's active workspace.
Open the Session View: Navigate to the pull request on GitHub and click the View session button.
This opens the step-by-step agent workflow and reasoning interface.
Inspect the Logs: Review the detailed execution log in the session timeline to identify exactly where the background task (running in GitHub Actions) stalled or encountered an error.
Steer or Prompt to Resume: Instead of unassigning and reassigning the issue (which completely restarts the task from scratch), use the chat input or steering tools directly inside the View session page or Agents panel to nudge Copilot. Providing a clarification prompt allows the agent to continue its current approach without losing completed steps.
Box 2: Post a pull request comment that mentions @copilot
You can resume the work by commenting on the pull request and mentioning @copilot, along with a prompt instructing it to continue.When a GitHub Copilot coding agent stalls or pauses on a draft pull request, posting a comment mentioning @copilot wakes the agent back up. Because the agent evaluates the entire conversation history, code changes, and task checklists within that pull request lifecycle, it will resume from its last saved state. It maintains the previously chosen approach and carries on with the remaining items without repeating already completed steps.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 67
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 # 68
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?

A. Wait for the agent to complete.
B. Select Approve and run workflows in the pull request merge box.
C. Unassign the issue from Copilot, and then reassign the issue to Copilot.
D. Merge the draft pull request and mention @copilot on the merged pull request.

Answer: C

Explanation:
To restart the background session and force the agent to resume its task, unassign the issue from GitHub Copilot and then reassign it to Copilot.
This specific operational cycle terminates the frozen cloud background process and launches a fresh agent session. Because a draft pull request already exists and is bound to the issue context, the newly initiated session automatically detects the linked branch, picks up the previous implementation plan, and begins pushing new commits directly to that existing draft PR.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent

NEW QUESTION # 69
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