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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement tool use and environment interaction | 20–25% | - Development environment integration
|
| Topic 2: Evaluation, error analysis, and tuning | 15–20% | - Tuning agent behavior
|
| Topic 3: Implement guardrails and accountability | 10–15% | - Guardrails and human-in-the-loop
|
| Topic 4: Orchestrate multi-agent coordination | 15–20% | - Observability and auditability
|
| Topic 5: Manage memory, state, and execution | 10–15% | - Cross-tool continuity
|
| Topic 6: Prepare agent architecture and SDLC processes | 15–20% | - Observability and control
|
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NEW QUESTION # 27
You have a GitHub repository that uses a GitHub Actions workflow to run an agent-driven change plan as part of a CI pipeline. The workflow generates an artifact named plan.json that includes a field named risk. risk has possible values of low, medium, or high.
You need to ensure that a human must confirm the execution of the workflow when risk is medium or high. The workflow must proceed automatically only when risk is low.
How should you complete the workflow? 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 # 28
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 resolve the scoping issue associated to agent1.
What should you do?
Answer: C
Explanation:
Litware identifies the following issues: During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
Creating a fine-grained Personal Access Token (PAT) and storing it as a GitHub Actions repository secret is a highly effective way to restrict a Copilot agent's access. By default, a GitHub Copilot cloud agent's native authentication token is limited strictly to the repository where it is running. However, if your agent utilizes agentic workflows or Model Context Protocol (MCP) integrations that pull broader data, injecting a localized PAT ensures it can never read files outside its explicitly designated scope.
Reference:
https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
NEW QUESTION # 29
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?
Answer: A
NEW QUESTION # 30
You have a GitHub repository that uses the GitHub Copilot coding agent. The repository uses strict branch protections on the main branch.
You maintain a long-lived branch named release/1.4 that has different dependencies and CI checks.
You need the agent to create a hotfix that is implemented and validated against the release/1.4 branch. The solution must prevent the agent from basing its work on main.
What should you do?
Answer: B
Explanation:
Selecting release/1.4 as the base branch establishes the correct repository context before the agent begins its assignment. GitHub documents that Copilot creates its working branch from the selected base branch. This ensures that the initial code, dependency declarations, and branch-specific files correspond to the release being repaired.
That distinction matters because a hotfix developed against main may depend on APIs, package versions, or build behavior that do not exist in the maintained release. Choosing the release branch at task creation removes the need for the agent to infer or subsequently correct its starting point. The task should also require validation using the release's applicable checks.
Option A controls concurrency rather than branch selection. Option B governs review requirements for changes to main; it does not determine where the agent starts. Option C supplies a natural-language instruction but is less direct than configuring the actual base branch before execution.
The selected answer configures the execution environment through the platform's supported task controls.
Relevant curriculum topics are branch-based scope, execution-context selection, and environment-specific constraints.
Reference:
NEW QUESTION # 31
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
NEW QUESTION # 32
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