Quiz 2026 Microsoft GH-600: Developing in Agentic AI Systems–The Best Key Concepts

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement tool use and environment interaction	20-25%	- Integrate agents within development environments 1. Configure an agent to handle environment-specific constraints 2. Configure an agent's scope to a specific repository 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Configure an agent to use branch-based scope 5. Configure an agent to be invoked in a CI workflow 6. Evaluate the execution context for an agent - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement traceability and accountability for agent actions 3. Implement retries 4. Implement rollbacks 5. Implement escalation paths - Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure the MCP registries 4. Configure MCP allow lists - Select and configure agent tools 1. Identify required tools 2. Configure agent tool permissions 3. Configure agent tools
Topic 2: Perform evaluation, error analysis, and tuning	15-20%	- 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. Generate evaluation signals by using automated scanning tools 4. Specify expected outcomes and operational constraints for agent tasks - 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 tool usage and tool access 2. Refine memory usage 3. Revise instructions, workflows, or constraints
Topic 3: 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. Preserve execution velocity by minimizing approvals that do not materially reduce risk 2. Identify the subset of actions that require human judgment 3. Scope permissions and execution contexts to enforce least-privilege access 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
Topic 4: Orchestrate multi-agent coordination	15-20%	- 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 - Manage the lifecycle of agents within multi-agent workflows 1. Retire agents while preserving auditability and workflow continuity 2. Add agents to existing multi-agent workflows 3. Update, reconfigure, or replace agents without disrupting active workflows - 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 - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Respond to degraded behavior or coordination across agents 3. Identify failed, partial, or stalled agent executions
Topic 5: Prepare agent architecture and SDLC processes	15-20%	- 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 checked and approved - 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. Configure human intervention for autonomous agents without slowing delivery 2. Configure agents to produce inspectable artifacts within standard development tooling 3. Plan and implement the degree of agent autonomy, including guardrails
Topic 6: Manage memory, state, and execution	10-15%	- Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Scope agent memory to task-relevant information 3. Choose between short-term, long-term, and external memory - 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

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Free PDF Quiz Reliable GH-600 - Developing in Agentic AI Systems Key Concepts

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Microsoft Developing in Agentic AI Systems Sample Questions (Q87-Q92):

NEW QUESTION # 87
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

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

Answer: C

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 # 88
You have a GitHub Enterprise Cloud organization that uses a custom coding agent to run GitHub Actions workflows that create branches, open pull requests, and merge changes after required checks pass.
You need to log all agent-initiated actions and ensure that the logs are retained for two years.
What should you do?

A. Configure audit log streaming.
B. Enable log forwarding.
C. Enable Git events in the audit log.

Answer: A

Explanation:
Audit log streaming is the correct choice because it exports audit events to an external destination where the organization can apply its required two-year retention policy. This captures agent-initiated activity as part of the organization's auditable GitHub events.
Standard audit-log retention in GitHub may not meet a long-term evidence requirement on its own. Streaming allows the organization to store and search the exported events in a SIEM, data lake, or logging platform with retention controls aligned to compliance policy.
Log forwarding is not the required GitHub audit mechanism in this scenario. Enabling Git events can increase event visibility, but it does not independently provide the requested long-term retention of all relevant agent-initiated actions.
The external destination should preserve actor details, timestamps, repository identifiers, action types, and correlation information needed to reconstruct agent activity during an investigation.
Study-guide topics: accountability, audit log streaming, retention, and agent activity traceability.

NEW QUESTION # 89
You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
How should you complete the YAML? 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 # 90
You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A. Switch the agent to --allow-all mode
B. Enable Copilot memory
C. Increase MCP server rate limits
D. Add clear, reproducible steps and expected behavior to the issue

Answer: D

NEW QUESTION # 91
Hotspot Question
Your company uses Microsoft Visual Studio Code and GitHub Copilot Chat.
You have a GitHub repository that uses main as the default branch. The repository contains a workspace custom agent stored at .github/agents/release-notes.agent.md.
A developer switches to a branch named branch1 where the agent file does NOT exist. In the same session, the developer switches to a user profile named profile1.profile1 contains a custom agent file named release-notes.agent.md that has user-invokable set to false.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: No
The agent will not appear in the agents dropdown with a display name of release-notes because the developer is currently on branch1 and profile1, which effectively hides it.
Box 2: No
The workspace version of the release-notes agent will not be available in the agents dropdown when the developer is on branch2.
Box 3: No
The user profile agent will not appear in the agents dropdown, nor will it override a workspace agent in this scenario.
Reference:
https://code.visualstudio.com/docs/agent-customization/custom-agents

NEW QUESTION # 92
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