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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
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. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Identify qualitative and quantitative evaluation signals to evaluate agents - 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. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access
Prepare agent architecture and SDLC processes	15-20%	- Configure observability and control for autonomous agents 1. Configure agents to produce inspectable artifacts within standard development tooling 2. Configure human intervention for autonomous agents without slowing delivery 3. Plan and implement the degree of agent autonomy, including guardrails - Integrate agents into the software development lifecycle (SDLC) 1. Identify steps for agents to perform 2. Identify and mitigate common anti-patterns in agents 3. Define inputs, outputs, and success criteria for agents - Define boundaries between planning, reasoning, and action 1. Configure an agent to output a structured plan 2. Validate agent plans 3. Prevent agent action until the agent checked and approved 4. Configure agent planning to be distinct from agent execution
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. 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 - 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 tool use and environment interaction	20-25%	- Operate agents with safe execution paths and robust error handling 1. Implement escalation paths 2. Implement retries 3. Implement traceability and accountability for agent actions 4. Implement error handling 5. Implement rollbacks - 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 - Select and configure agent tools 1. Configure agent tools 2. Configure agent tool permissions 3. Identify required tools - Integrate agents within development environments 1. Configure an agent to handle environment-specific constraints 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 be invoked in a CI workflow
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. Preserve execution velocity by minimizing approvals that do not materially reduce risk 3. Block actions that violate defined security, compliance, or Responsible AI policies 4. Scope permissions and execution contexts to enforce least-privilege access 5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
Orchestrate multi-agent coordination	15-20%	- 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 - Detect and respond to multi-agent failures and degraded behavior 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 2. Identify failed, partial, or stalled agent executions 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 - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior

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Microsoft Developing in Agentic AI Systems Sample Questions (Q70-Q75):

NEW QUESTION # 70
Drag and Drop Question
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:

Explanation:
Box 1: pre-exec
The hook needs to inspect and validate the generated shell commands before they are allowed to execute. The pre-exec event interceptor serves exactly this guardrail purpose. (post-exec would run only after the command has already finished).
Box 2: .copilot/hooks.yaml
The config property under the cli.hooks block points to the local path of the hooks configuration file itself so that the CLI can parse and load the defined Reference:
https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-best-practices

NEW QUESTION # 71
You assigned an issue to the Copilot coding agent, and it opened a pull request. You want to inspect exactly what code changes were made before merging. Which CLI slash command lets you view the change set directly in the terminal?

A. /compact
B. /diff
C. /plan
D. /context

Answer: B

Explanation:
/diff shows the file-level changes the agent has made during the session, letting you review modifications before committing or merging, without leaving the terminal.

NEW QUESTION # 72
Case Study 1 - Contoso, Ltd
Overview
Contoso Ltd. is a software development company located in the United States.
Existing Environment
GitHub Environment
Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1that contains the following:
- A custom agent named agent1 that includes instructions to review specs related to best practices
- A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files
- A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
- The front-end is stored in the /frontend folder.
- The API logic is stored in the /api folder.
Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
Problem Statements
The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
Agent Logs
You have the following logs for the multi-agent workflow used in repo2.

Requirements
Planned Changes
Contoso plans to have all agents and developers in repo1use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
Technical Requirements
App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
All AI-generated code for UI styling must adhere to a predefined folder structure.
The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.
Hotspot Question
You need to implement agent2 to meet the technical requirements.
How should you complete the YAML configuration? 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: 'search',
Search allows the agent to look for specific keywords, classes, or patterns across your repository to understand the current structure and code requirements.
Box 2: 'read'
Read grants the agent read-only permission to examine the full contents of the codebase files without having the capability to alter them.
Reference:
https://github.com/github/copilot-cli/issues/1663

NEW QUESTION # 73
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to implement changes by creating draft pull requests in a firewalled GitHub Actions environment.
Repository administrators add a Model Context Protocol (MCP) server configuration so that the agent can query an external system while it executes issues. The MCP server requires an API key, and the key must be provided to the MCP server as an environment variable when the server starts.
You create an environment secret named copilot_mcp_api_key that contains the API key.
You need to configure the repository to ensure that the MCP server receives the API key at runtime. The solution must ensure that only the intended secret is available to the MCP configuration.
What is the best option to use to achieve the goal?

A. A default environment variable
B. An environment variable mapping
C. The JSON configuration
D. An environment variable
E. An Actions repository secret

Answer: B

Explanation:
An environment variable mapping connects the defined environment secret to the exact environment variable required by the MCP server at startup. This allows the MCP server to receive the API key without embedding the secret's value in repository configuration.
A default environment variable is not sufficiently targeted because it can expose data more broadly than necessary. An Actions repository secret is a secret storage mechanism, but the scenario already uses an environment secret and requires controlled injection into the MCP server process. The JSON configuration should reference the mapping or variable name; it must not contain the raw key value.
The mapping provides the least-privilege boundary: only the intended secret is made available, under the expected variable name, to the MCP process that needs it. It also supports credential rotation because the stored secret can be replaced without modifying committed MCP configuration.
Study-guide topics: MCP credential injection, environment secrets, least privilege, and secure tool startup.

NEW QUESTION # 74
A pull request created by the Copilot coding agent needs to be automatically closed if it fails to pass required checks after three retry attempts, per your organization's governance policy. What should you configure?

A. .copilotignore
B. A repository ruleset with agent-specific merge conditions and CI enforcement
C. /compact
D. agents.md

Answer: B

Explanation:
Repository rulesets allow organizations to define enforcement policies - including required status checks - that apply uniformly to pull requests, including those opened by autonomous agents, ensuring failing agent PRs cannot be merged.

NEW QUESTION # 75
......

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