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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement guardrails and accountability | 10–15% | - Implement guardrails and human-in-the-loop workflows
|
| Topic 2: Implement tool use and environment interaction | 20–25% | - Operate agents with safe execution paths and robust error handling
|
| Topic 3: Orchestrate multi-agent coordination | 15–20% | - Operate and manage multi-agent workflows
|
| Topic 4: Manage memory, state, and execution | 10–15% | - Ensure continuity of agent memory and state across tools and environments
|
| Topic 5: Perform evaluation, error analysis, and tuning | 15–20% | - Tune agent behavior based on evaluation results
|
| Topic 6: Prepare agent architecture and SDLC processes | 15–20% | - Configure observability and control for autonomous agents
|
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NEW QUESTION # 66
A developer uses the GitHub Copilot CLI in plan mode.
Copilot produces a plan.
What does Copilot do next?
Answer: A
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 # 67
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
agent-run that generates trace.json and plan.md
review that waits for human approval before continuing
deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
Answer: D
Explanation:
Workflow artifacts provide the required combination of retained execution evidence and file transfer between jobs. The agent-run job uploads trace.json and plan.md, associating both files with the workflow run. Reviewers can access the resulting artifact through the run's GitHub Actions interface, while downstream jobs download it to their own execution environments.
This is necessary because separate jobs do not automatically share a local filesystem. Declaring a dependency establishes execution order but does not transfer the generated files. The review and deployment stages therefore need explicit artifact downloads in addition to their appropriate job dependencies.
Committing transient traces and plans introduces repository changes solely to transport run-specific evidence. A network share adds external infrastructure and does not directly satisfy the requirement for standard Actions artifact access. Dependency caching is intended for reusable inputs such as downloaded dependencies; it is not the appropriate mechanism for authoritative outputs from a particular agent run.
Artifacts preserve the plan and trace that informed the review, allowing subsequent deployment activity to use the corresponding files. Relevant curriculum topics are inspectable agent artifacts, observability, and human intervention.
Reference:
NEW QUESTION # 68
You have a GitHub Enterprise Cloud organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals.
Answer:
Explanation:
NEW QUESTION # 69
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?
Answer: B
Explanation:
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
NEW QUESTION # 70
You have a GitHub Copilot Enterprise subscription. Developers use Microsoft Visual Studio Code and GitHub Copilot.
You have the following Model Context Protocol (MCP) configuration in Visual Studio Code:
{
"servers": {
"mcp1": {
"command": "npx",
"args": ["-y", "@microsoft/mcp-server-test"]
},
"mcp2": {
"command": "uvx",
"args": ["mcp-server-test", "--db-path", "pubs.db"]
}
}
}
For each statement, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
NEW QUESTION # 71
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