100% Pass Quiz 2026 Microsoft Reliable GH-600: Developing in Agentic AI Systems Exam Questions Answers

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Manage memory, state, and execution	10–15%	- 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Prevent conflicting context 3. Share agent state - Implement agent memory strategies 1. Scope agent memory to task-relevant information 2. Define memory expiration, pruning, and reset rules 3. Choose between short-term, long-term, and external memory
Implement guardrails and accountability	10–15%	- Implement guardrails and human-in-the-loop workflows 1. Block actions that violate defined security, compliance, or Responsible AI policies 2. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Identify the subset of actions that require human judgment 4. Scope permissions and execution contexts to enforce least-privilege access 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%	- 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 an agent to output a structured plan 2. Prevent agent action until the agent checks and approves 3. Validate agent plans 4. Configure agent planning to be distinct from agent execution - 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
Perform evaluation, error analysis, and tuning	15–20%	- 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. Generate evaluation signals by using automated scanning tools 4. Specify expected outcomes and operational constraints for agent tasks - 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
Implement tool use and environment interaction	20–25%	- Select and configure agent tools 1. Configure agent tools 2. Identify required tools 3. Configure agent tool permissions - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement error handling 3. Implement rollbacks 4. Implement escalation paths 5. Implement retries - Integrate agents within development environments 1. Configure an agent's scope to a specific repository 2. Configure an agent to handle environment-specific constraints 3. Evaluate the execution context for an agent 4. Enable an agent to perform autonomous actions, including creating branches and pull requests 5. Configure an agent to use branch-based scope 6. Configure an agent to be invoked in a CI workflow - Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure MCP registries 3. Configure a GitHub remote MCP server 4. Configure MCP allow lists
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. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions - 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 - Operate and manage multi-agent workflows 1. Apply an orchestration pattern to coordinate multiple agents 2. Configure agent isolation for parallel execution 3. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs - Manage the lifecycle of agents within multi-agent workflows 1. Add agents to existing multi-agent workflows 2. Retire agents while preserving auditability and workflow continuity 3. Update, reconfigure, or replace agents without disrupting active workflows

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Microsoft Developing in Agentic AI Systems Sample Questions (Q54-Q59):

NEW QUESTION # 54
You have a GitHub repository.
Developers use the GitHub Copilot CLI and repository-scoped hooks under .github/hooks/*.json.
You need to allow the Copilot CLI to automatically run low-risk Bash commands. The solution must prevent the autonomous execution of high-risk commands, such as sudo, rm -rf /, and curl ... | bash.
What should you do?

A. Configure a preToolUse hook that returns permissionDecision: "deny".
B. Add .github/hooks/logs/ to .gitignore.
C. Configure a sessionStart hook that prints a policy banner.
D. Configure a userPromptSubmitted hook that logs prompts and exits nonzero.

Answer: A

Explanation:
To safely restrict high-risk commands while allowing low-risk execution, you must configure a preToolUse hook that returns permissionDecision: "deny" when a restricted pattern is detected.
The preToolUse lifecycle hook executes immediately before GitHub Copilot CLI attempts to invoke any tool or execute a command. It evaluates the tool arguments and can programmatically intercept execution by writing a specific JSON payload to stdout. Returning
{"permissionDecision": "deny"} explicitly instructs Copilot CLI to block the autonomous execution of that command.
Reference:
https://code.visualstudio.com/docs/agent-customization/hooks

NEW QUESTION # 55
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?

A. creates a branch named /copilot/plan in the repository
B. begins the implementation
C. opens a pull request that has the plan as a comment
D. saves the plan to plan.md

Answer: D

Explanation:
In plan mode, after GitHub Copilot CLI analyzes your codebase and gathers requirements, it saves the plan to plan.md in your session folder.
What Happens Next?
1. Saves to plan.md: It writes the structured implementation plan with checkable tasks into a file called plan.md.
2. Waits for Approval: Copilot pauses and waits for your review. It will not begin implementation or make code modifications until you explicitly approve the strategy.
3. Execution: Once approved, you can hand the plan off to Copilot's agent/autopilot mode to begin the implementation.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices

NEW QUESTION # 56
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?

A. Run gh agent-task list and specify the --web parameter.
B. Run gh agent-task view and specify the --log and --jq parameters.
C. Run gh agent-task list and specify the --status and --jq parameters.
D. Run gh agent-task view and specify the --log and --follow parameters.

Answer: D

Explanation:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents

NEW QUESTION # 57
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.

Answer:

Explanation:

NEW QUESTION # 58
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:
In VS Code agent mode, GitHub Copilot can use built-in tools to perform actions required to complete and validate a development task. One of these capabilities is the terminal tool, which allows the agent to execute commands such as builds, linters, package-management operations, and test suites directly through VS Code's integrated terminal. The user can explicitly instruct the agent to make changes and then run a designated test command to verify the result.
The relevant control is the agent's tool and terminal invocation permissions. Depending on configured approval policies, Copilot can request authorization for a terminal command or execute approved commands as part of its iterative workflow. This allows validation to become part of the agent's task rather than requiring the developer to manually open the terminal after every modification. GitHub documentation specifically describes agent mode as capable of making edits, issuing terminal commands, observing results, and iterating when necessary.
Option A is unnecessary because running a local test suite does not require an MCP server. .copilotignore concerns context/access exclusions, while CODEOWNERS defines repository ownership and review responsibilities rather than agent execution capabilities.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; terminal tools; agent permissions; automated validation; iterative tool execution.

NEW QUESTION # 59
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