素敵なGH-600復習問題集 &合格スムーズGH-600日本語版トレーリング |実用的なGH-600無料サンプルDeveloping in Agentic AI Systems: Startsida: delevac407

素敵なGH-600復習問題集 &合格スムーズGH-600日本語版トレーリング |実用的なGH-600無料サンプルDeveloping in Agentic AI Systems

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GoShikenはIT認定試験のGH-600問題集を提供して皆さんを助けるウエブサイトです。GoShikenは先輩の経験を生かして暦年の試験の材料を編集することを通して、最高のGH-600問題集を作成しました。問題集に含まれているものは実際試験の問題を全部カバーすることができますから、あなたが一回で成功することを保証できます。

Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Add an MCP server as a tool to an agent 2. Configure a GitHub remote MCP server 3. Configure MCP registries 4. Configure MCP allow lists - Integrate agents within development environments 1. Configure an agent to use branch-based scope 2. Enable an agent to perform autonomous actions, including creating branches and pull requests 3. Configure an agent to be invoked in a CI workflow 4. Evaluate the execution context for an agent 5. Configure an agent to handle environment-specific constraints 6. Configure an agent's scope to a specific repository - Operate agents with safe execution paths and robust error handling 1. Implement traceability and accountability for agent actions 2. Implement rollbacks 3. Implement escalation paths 4. Implement error handling 5. Implement retries - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions
Manage memory, state, and execution	10–15%	- Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Detect and correct drift during extended agent execution 3. Resume agent work without repeating steps or diverging from prior decisions - 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 - Ensure continuity of agent memory and state across tools and environments 1. Prevent conflicting context 2. Prevent stale context 3. Share agent state
Perform evaluation, error analysis, and tuning	15–20%	- Define success criteria and evaluation signals for agent tasks 1. Specify expected outcomes and operational constraints for agent tasks 2. Identify qualitative and quantitative evaluation signals to evaluate agents 3. Align evaluation criteria with development intent 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 - Tune agent behavior based on evaluation results 1. Revise instructions, workflows, or constraints 2. Refine memory usage 3. Refine tool usage and tool access
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 - 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. Perform post-hoc analysis of multi-agent behavior 3. Document key decisions, handoffs, and outcomes across agents - 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 - 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
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. Scope permissions and execution contexts to enforce least-privilege access 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. 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
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. Validate agent plans 3. Prevent agent action until the agent checks and approves 4. Configure agent planning to be distinct from agent execution - 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 - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Define inputs, outputs, and success criteria for agents 3. Identify steps for agents to perform

>> GH-600復習問題集 <<

GH-600復習問題集: Developing in Agentic AI Systemsとても実用的GH-600日本語版トレーリング

さまざまな人々がさまざまな学習習慣を持っているという事実を踏まえて、3つのGH-600トレーニング質問バージョンをご案内します。さらに、GH-600学習教材のデモを自由にダウンロードして検討することもできます。そのような試用に追加料金は発生しないことをお約束します。逆に、GH-600試験問題のデモを試して、十分な内容を選択することを心からお勧めします。 GH-600トレーニングガイドは、時間とお金をかけて購入する価値があります。

Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q80-Q85):

質問 # 80
You are evaluating the logs of the multi-agent workflow in repo2.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 81
Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A. Store the API key as a GitHub Codespaces user secret scoped to product-api.
B. In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.
C. Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
D. In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.

正解：C

解説：
Scenario:
Agent environment: A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
Security requirement: All API keys must be stored and accessed securely.
The correct solution is to store the API key as an Agents secret in the Copilot environment of the repository using the COPILOT_MCP_ name prefix, and then reference it in your MCP configuration.
Strict Prefix Enforcement: GitHub Copilot cloud agent isolates execution for security. It will only expose secrets and variables that explicitly begin with the COPILOT_MCP_ prefix to the MCP server configuration.
Environment Alignment: Storing it as a native Copilot agent secret ensures that when the remote Copilot agent spins up to execute your JSON configuration, it can securely bind and decrypt the secret directly into the server's runtime environment variables.
Config Separation: This practice keeps your sensitive production tokens entirely out of version- controlled mcp.json or .vscode/mcp.json tracking files.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers

質問 # 82
You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter:
---
name: test-agent
description: Custom agent description
tools: ['tool-a', 'tool-b']
---
In the same repository, you have an MCP configuration file named mcp.json.
You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset.
What should you do?

A. Replace "servers": with "mcp-servers": in mcp.json.
B. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].
C. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].
D. Replace line 04 with tools: ['copilot/*'].

正解：D

解説：
The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent.
Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use.
Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions.
Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access.

質問 # 83
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. CodeQL findings that identify security vulnerabilities in the generated code
B. the median time to merge metric reported on the Copilot usage metrics dashboard
C. comments from Copilot code review that suggest improvements to code patterns
D. linting errors from the repository's CI pipeline configured in GitHub Actions
E. the detection of hardcoded secrets, such as API keys and tokens

正解：A、E

解説：
The two automated evaluation signals generated by the coding agent's built-in scanning tools are:
CodeQL findings that identify security vulnerabilities in the generated code.
The detection of hardcoded secrets, such as API keys and tokens.
Reference:
https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/

質問 # 84
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to resolve backlog issues by creating draft pull requests. The repository uses a GitHub Actions workflow for deployments to the production environment.
You discover that the workflow is being triggered before human review.
You need to configure GitHub controls to meet the following requirements:
The workflow for the agent's draft pull requests must NOT run until a user that has write access approves the pull requests.
Production deployment jobs must start only after a user with write access explicitly approves the jobs.
Which controls should you configure? To answer, drag the appropriate controls to the correct requirements.

正解：

解説：

質問 # 85
......

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[{:section_type=>"html", :content=>"GH-600復習問題集, GH-600日本語版トレーリング, GH-600無料サンプル, GH-600日本語版サンプル, GH-600ウェブトレーニング\n<a href=\"https://www.goshiken.com/Microsoft/GH-600-mondaishu.html\"><img src=\"https://ts2.mm.bing.net/th?w=500&amp;q=Developing%20in%20Agentic%20AI%20Systems\"></a>\nGoShikenはIT認定試験のGH-600問題集を提供して皆さんを助けるウエブサイトです。GoShikenは先輩の経験を生かして暦年の試験の材料を編集することを通して、最高の<a href=\"https://jp.fast2test.com/GH-600-premium-file.html\">GH-600問題集</a>を作成しました。問題集に含まれているものは実際試験の問題を全部カバーすることができますから、あなたが一回で成功することを保証できます。<h2>Microsoft GH-600 Exam Syllabus Topics:</h2><table class=\"mytable\" border=\"1\" cellpadding=\"1\" cellspacing=\"1\" style=\"width: 100%; border-color: #E0E0E0;\"><tbody>\n<tr>\n<th>Section</th>\n<th>Weight</th>\n<th>Objectives</th>\n</tr>\n<tr>\n<td>Implement tool use and environment interaction</td>\n<td>20–25%</td>\n<td>- Configure MCP servers <ul>\n<li>1. Add an MCP server as a tool to an agent<ul></ul>\n</li>\n<li>2. Configure a GitHub remote MCP server<ul></ul>\n</li>\n<li>3. Configure MCP registries<ul></ul>\n</li>\n<li>4. Configure MCP allow lists<ul></ul>\n</li>\n</ul>- Integrate agents within development environments <ul>\n<li>1. Configure an agent to use branch-based scope<ul></ul>\n</li>\n<li>2. Enable an agent to perform autonomous actions, including creating branches and pull requests<ul></ul>\n</li>\n<li>3. Configure an agent to be invoked in a CI workflow<ul></ul>\n</li>\n<li>4. Evaluate the execution context for an agent<ul></ul>\n</li>\n<li>5. Configure an agent to handle environment-specific constraints<ul></ul>\n</li>\n<li>6. Configure an agent's scope to a specific repository<ul></ul>\n</li>\n</ul>- Operate agents with safe execution paths and robust error handling <ul>\n<li>1. Implement traceability and accountability for agent actions<ul></ul>\n</li>\n<li>2. Implement rollbacks<ul></ul>\n</li>\n<li>3. Implement escalation paths<ul></ul>\n</li>\n<li>4. Implement error handling<ul></ul>\n</li>\n<li>5. Implement retries<ul></ul>\n</li>\n</ul>- Select and configure agent tools <ul>\n<li>1. Identify required tools<ul></ul>\n</li>\n<li>2. Configure agent tools<ul></ul>\n</li>\n<li>3. Configure agent tool permissions<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n<tr>\n<td>Manage memory, state, and execution</td>\n<td>10–15%</td>\n<td>- Persist agent state and manage context drift <ul>\n<li>1. Capture task progress and decisions as durable artifacts<ul></ul>\n</li>\n<li>2. Detect and correct drift during extended agent execution<ul></ul>\n</li>\n<li>3. Resume agent work without repeating steps or diverging from prior decisions<ul></ul>\n</li>\n</ul>- Implement agent memory strategies <ul>\n<li>1. Scope agent memory to task-relevant information<ul></ul>\n</li>\n<li>2. Define memory expiration, pruning, and reset rules<ul></ul>\n</li>\n<li>3. Choose between short-term, long-term, and external memory<ul></ul>\n</li>\n</ul>- Ensure continuity of agent memory and state across tools and environments <ul>\n<li>1. Prevent conflicting context<ul></ul>\n</li>\n<li>2. Prevent stale context<ul></ul>\n</li>\n<li>3. Share agent state<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n<tr>\n<td>Perform evaluation, error analysis, and tuning</td>\n<td>15–20%</td>\n<td>- Define success criteria and evaluation signals for agent tasks <ul>\n<li>1. Specify expected outcomes and operational constraints for agent tasks<ul></ul>\n</li>\n<li>2. Identify qualitative and quantitative evaluation signals to evaluate agents<ul></ul>\n</li>\n<li>3. Align evaluation criteria with development intent<ul></ul>\n</li>\n<li>4. Generate evaluation signals by using automated scanning tools<ul></ul>\n</li>\n</ul>- Analyze agent failures and identify root causes <ul>\n<li>1. Identify failures by using logs, plans, traces, outputs, and workflow artifacts<ul></ul>\n</li>\n<li>2. Classify root causes, including reasoning errors, tool misuse, and context or environment issues<ul></ul>\n</li>\n</ul>- Tune agent behavior based on evaluation results <ul>\n<li>1. Revise instructions, workflows, or constraints<ul></ul>\n</li>\n<li>2. Refine memory usage<ul></ul>\n</li>\n<li>3. Refine tool usage and tool access<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n<tr>\n<td>Orchestrate multi-agent coordination</td>\n<td>15–20%</td>\n<td>- Operate and manage multi-agent workflows <ul>\n<li>1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs<ul></ul>\n</li>\n<li>2. Configure agent isolation for parallel execution<ul></ul>\n</li>\n<li>3. Apply an orchestration pattern to coordinate multiple agents<ul></ul>\n</li>\n</ul>- Configure observability for multi-agent behavior by using logs, artifacts, and operational signals <ul>\n<li>1. Configure multi-agent workflows to produce artifacts suitable for review and audit<ul></ul>\n</li>\n<li>2. Perform post-hoc analysis of multi-agent behavior<ul></ul>\n</li>\n<li>3. Document key decisions, handoffs, and outcomes across agents<ul></ul>\n</li>\n</ul>- Manage the lifecycle of agents within multi-agent workflows <ul>\n<li>1. Add agents to existing multi-agent workflows<ul></ul>\n</li>\n<li>2. Retire agents while preserving auditability and workflow continuity<ul></ul>\n</li>\n<li>3. Update, reconfigure, or replace agents without disrupting active workflows<ul></ul>\n</li>\n</ul>- Detect and respond to multi-agent failures and degraded behavior <ul>\n<li>1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop<ul></ul>\n</li>\n<li>2. Respond to degraded behavior or coordination across agents<ul></ul>\n</li>\n<li>3. Identify failed, partial, or stalled agent executions<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n<tr>\n<td>Implement guardrails and accountability</td>\n<td>10–15%</td>\n<td>- Implement guardrails and human-in-the-loop workflows <ul>\n<li>1. Block actions that violate defined security, compliance, or Responsible AI policies<ul></ul>\n</li>\n<li>2. Scope permissions and execution contexts to enforce least-privilege access<ul></ul>\n</li>\n<li>3. Identify the subset of actions that require human judgment<ul></ul>\n</li>\n<li>4. Preserve execution velocity by minimizing approvals that do not materially reduce risk<ul></ul>\n</li>\n<li>5. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes<ul></ul>\n</li>\n</ul>- Define autonomy levels <ul>\n<li>1. Classify agent actions by operational, security, and compliance risk to right-size human interventions<ul></ul>\n</li>\n<li>2. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n<tr>\n<td>Prepare agent architecture and SDLC processes</td>\n<td>15–20%</td>\n<td>- Define boundaries between planning, reasoning, and action <ul>\n<li>1. Configure an agent to output a structured plan<ul></ul>\n</li>\n<li>2. Validate agent plans<ul></ul>\n</li>\n<li>3. Prevent agent action until the agent checks and approves<ul></ul>\n</li>\n<li>4. Configure agent planning to be distinct from agent execution<ul></ul>\n</li>\n</ul>- Configure observability and control for autonomous agents <ul>\n<li>1. Configure human intervention for autonomous agents without slowing delivery<ul></ul>\n</li>\n<li>2. Plan and implement the degree of agent autonomy, including guardrails<ul></ul>\n</li>\n<li>3. Configure agents to produce inspectable artifacts within standard development tooling<ul></ul>\n</li>\n</ul>- Integrate agents into the software development lifecycle (SDLC) <ul>\n<li>1. Identify and mitigate common anti-patterns in agents<ul></ul>\n</li>\n<li>2. Define inputs, outputs, and success criteria for agents<ul></ul>\n</li>\n<li>3. Identify steps for agents to perform<ul></ul>\n</li>\n</ul>\n</td>\n</tr>\n</tbody></table><a href=\"https://www.goshiken.com/Microsoft/GH-600-mondaishu.html\" style=\"background-color: #d2322d; color: #FFFFFF; text-decoration: none; padding: 10px; font-weight: bold;\" target=\"_blank\">&gt;&gt; GH-600復習問題集 &lt;&lt;</a><h2>GH-600復習問題集: Developing in Agentic AI Systemsとても実用的GH-600日本語版トレーリング</h2>さまざまな人々がさまざまな学習習慣を持っているという事実を踏まえて、3つのGH-600トレーニング質問バージョンをご案内します。さらに、GH-600学習教材のデモを自由にダウンロードして検討することもできます。そのような試用に追加料金は発生しないことをお約束します。逆に、<a href=\"https://www.passtest.jp/Microsoft/GH-600-shiken.html\">GH-600試験問題</a>のデモを試して、十分な内容を選択することを心からお勧めします。 GH-600トレーニングガイドは、時間とお金をかけて購入する価値があります。<h2>Microsoft Developing in Agentic AI Systems 認定 GH-600 試験問題 (Q80-Q85):</h2>質問 # 80 You are evaluating the logs of the multi-agent workflow in repo2. For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. <img style=\"max-width: 100%;\" src=\"https://www.goshiken.com/uploads/imgs/GH-600-d7ccb5f221c713f2a269509ac706017c.jpg\">正解：解説： <img style=\"max-width: 100%;\" src=\"https://www.goshiken.com/uploads/imgs/GH-600-4f980840a85235ead4dbe553931083d5.jpg\"> 質問 # 81 Case Study 2 Existing Environment GitHub Environment The GitHub environment contains the following: - Three repositories named product-api, billing-service, and infra-terraform. - Branch protection on the main branch in all repositories that requires at least one pull request review before merging - GitHub Actions runners used across all workflows - A GitHub team named SG_Dev that contains developers - A GitHub team named SG_Review that contains senior engineers and a security team - A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations: - No custom agent profile is defined. - A Model Context Protocol (MCP) server named MCP1 is deployed to https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs. MCP1 requires an API key for authentication. A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues. Copilot memory is NOT enabled for the organization. Problem Statements Litware identifies the following issues: - During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope. - agent1 makes code changes immediately after receiving a task. - A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes. Other developers report this intermittently as well. - Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs. agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev. Requirements Planned Changes Litware plans to make the following changes: - Ensure that agent1 can access all the tools in the environment. - Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review. - Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration. - Ensure that Copilot retains details that it has learned and uses that knowledge for future work. This must be applied to all licensed members of the organization. Implementation guidelines The development team at Litware identifies the following implementation guidelines: - Agent workflows must be able to run in parallel. - Application error handling must use the repository ErrorHandler class. - agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently. Security requirements Litware identifies the following security requirements: - Only the members of SG_Review must be able to approve agent1 plan outputs. - All API keys must be stored and accessed securely. - The developers must NOT be able to self-approve. Agent configuration <img style=\"max-width: 100%;\" src=\"https://www.goshiken.com/uploads/imgs/GH-600-04e7c17c0f53b35e7fd3f661a8de9990.jpg\"> You need to provide access to the API key of MCP1. The solution must meet the security requirements. What should you do?<ul>\n<li>A. Store the API key as a GitHub Codespaces user secret scoped to product-api.</li>\n<li>B. In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.</li>\n<li>C. Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.</li>\n<li>D. In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.</li>\n</ul>正解：C解説： Scenario: Agent environment: A Model Context Protocol (MCP) server named MCP1 is deployed to https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs. MCP1 requires an API key for authentication. Security requirement: All API keys must be stored and accessed securely. The correct solution is to store the API key as an Agents secret in the Copilot environment of the repository using the COPILOT_MCP_ name prefix, and then reference it in your MCP configuration. Strict Prefix Enforcement: GitHub Copilot cloud agent isolates execution for security. It will only expose secrets and variables that explicitly begin with the COPILOT_MCP_ prefix to the MCP server configuration. Environment Alignment: Storing it as a native Copilot agent secret ensures that when the remote Copilot agent spins up to execute your JSON configuration, it can securely bind and decrypt the secret directly into the server's runtime environment variables. Config Separation: This practice keeps your sensitive production tokens entirely out of version- controlled mcp.json or .vscode/mcp.json tracking files. Reference: https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/configure-mcp-servers 質問 # 82 You have a custom agent profile file named test-agent.agent.md that contains the following YAML frontmatter: --- name: test-agent description: Custom agent description tools: ['tool-a', 'tool-b'] --- In the same repository, you have an MCP configuration file named mcp.json. You need to ensure that the GitHub Model Context Protocol (MCP) server is available to test the agent. The solution must allow only the Copilot toolset. What should you do?<ul>\n<li>A. Replace \"servers\": with \"mcp-servers\": in mcp.json.</li>\n<li>B. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/copilot'].</li>\n<li>C. Replace line 04 with tools: ['tool-a', 'tool-b', 'github/*'].</li>\n<li>D. Replace line 04 with tools: ['copilot/*'].</li>\n</ul>正解：D解説： The copilot/* toolset grants the agent access to the Copilot-provided tools while avoiding unrelated tool groups. This directly meets the requirement to allow only the Copilot toolset for the test agent. Keeping tool-a and tool-b would expand access beyond the stated requirement. Using github/* would expose the broader GitHub tool group rather than limiting the profile to the Copilot toolset. The MCP configuration retains its server structure; changing the server property name does not configure which tools the agent may use. Toolsets are an important control surface for specialized agents. They let an architect define a narrow capability set for testing, planning, review, or implementation roles. The selected toolset should still be backed by an available and authorized MCP configuration; naming a toolset does not bypass server authentication or policy restrictions. Study-guide topics: MCP toolsets, custom agent capabilities, and least-privilege tool access. 質問 # 83 You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository. Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review. You review the session log and the pull request. What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.<ul>\n<li>A. CodeQL findings that identify security vulnerabilities in the generated code</li>\n<li>B. the median time to merge metric reported on the Copilot usage metrics dashboard</li>\n<li>C. comments from Copilot code review that suggest improvements to code patterns</li>\n<li>D. linting errors from the repository's CI pipeline configured in GitHub Actions</li>\n<li>E. the detection of hardcoded secrets, such as API keys and tokens</li>\n</ul>正解：A、E解説： The two automated evaluation signals generated by the coding agent's built-in scanning tools are: CodeQL findings that identify security vulnerabilities in the generated code. The detection of hardcoded secrets, such as API keys and tokens. Reference: https://itacademy.com.ua/en/articles/2026-06-11/security-validation-third-party-coding-agents-github/ 質問 # 84 You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to resolve backlog issues by creating draft pull requests. The repository uses a GitHub Actions workflow for deployments to the production environment. You discover that the workflow is being triggered before human review. You need to configure GitHub controls to meet the following requirements: The workflow for the agent's draft pull requests must NOT run until a user that has write access approves the pull requests. Production deployment jobs must start only after a user with write access explicitly approves the jobs. Which controls should you configure? 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