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| Section | Weight | Objectives |
|---|---|---|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
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NEW QUESTION # 55
You want to grant the Copilot coding agent access to only a narrowly scoped set of repository permissions (e.g., read code, write pull requests) rather than full admin access. What governs this scope?
Answer: A
Explanation:
The coding agent operates using a scoped GitHub token whose permissions are configured at the repository or organization level, following the principle of least privilege -- granting only what's needed (e.g., contents: write, pull-requests: write).
NEW QUESTION # 56
You are running the GitHub Copilot CLI and want the agent to execute file edits and shell commands without prompting for confirmation on each individual action, during a trusted throwaway sandbox session. Which flag/command should you use?
Answer: B
Explanation:
The correct selection is --allow-all. GitHub Copilot CLI uses a permission model for potentially consequential operations such as invoking tools, modifying files, accessing filesystem paths, running shell commands, and reaching external URLs. Normally, these operations can require explicit approval. The --allow-all startup option grants all available permissions for the session, allowing Copilot to execute those operations without prompting for approval on each individual action. GitHub also provides --yolo as an alias for the same permission configuration.
This option is appropriate only when the execution environment is sufficiently isolated and trusted. GitHub specifically recommends using broad permissions in a sandbox or similarly disposable environment because the agent can modify or delete files and execute commands autonomously once approval barriers are removed.
/compact manages conversation context rather than permissions. --agent=Task selects an agent profile and does not globally pre-authorize tool execution. The GH-600 study guide explicitly assesses configuring agent tools, tool permissions, execution contexts, and safe autonomous actions, making permission scoping a core operational skill.
Study Guide Reference Topics: Implement Tool Use and Environment Interaction; configure agent tool permissions; evaluate execution context; safe autonomous execution.
NEW QUESTION # 57
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 troubleshoot the issue reported by Ben.
What should you review?
Answer: B
Explanation:
Scenario: 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.
To investigate a successful Copilot agent session that created an empty pull request, you should check the agent session log in the Agents panel.
Internal behavior: The empty pull request means the agent completed its logical execution loop without generating or committing code modifications.
Session context: The agent session log contains the specific LLM prompts, tool execution outputs, and file parsing steps that explain why the agent decided no changes were necessary.
Reference:
https://github.blog/ai-and-ml/github-copilot/whats-new-with-github-copilot-coding-agent/
NEW QUESTION # 58
You want the Copilot coding agent to scan a large codebase and propose a full, human- reviewable step-by-step plan before writing any code. What should you do first?
Answer: D
Explanation:
Plan mode lets the agent operate in a read-only analysis state, producing a structured Markdown plan of intended changes. This allows you to catch architectural issues before the agent starts editing files.
NEW QUESTION # 59
You need to resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?
Answer: A
Explanation:
Separate branches isolate each agent's proposed changes and allow the team to inspect them independently. Adding a required status check for file-level overlap detects conflicting modifications before either pull request is merged, preventing one agent's work from silently overwriting another's changes.
A read-only tool configuration prevents both agents from making required changes, so it avoids conflict by eliminating implementation capability rather than controlling it. CODEOWNERS approval adds review accountability for a specific file but does not automatically detect overlap between independent agent outputs. A single concurrency group serializes entire workflows and reduces throughput even where the agents work on unrelated files.
The selected solution maintains parallel development while creating a merge-time control for the actual risk: overlapping file changes. It also produces reviewable evidence of the overlap check in pull request status results.
Branch isolation does not eliminate semantic conflicts, such as incompatible API assumptions. Teams should therefore combine overlap detection with normal pull request review and integration testing.
Study-guide topics: parallel agent coordination, branch isolation, merge controls, and conflict detection.
NEW QUESTION # 60
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