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| Section | Weight | Objectives |
|---|---|---|
| Perform evaluation, error analysis, and tuning | 15-20% | - Define success criteria and evaluation signals for agent tasks
|
| Manage memory, state, and execution | 10-15% | - Persist agent state and manage context drift
|
| Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
| Implement tool use and environment interaction | 20-25% | - Operate agents with safe execution paths and robust error handling
|
| Orchestrate multi-agent coordination | 15-20% | - Detect and respond to multi-agent failures and degraded behavior
|
| Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
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NEW QUESTION # 82
An agent working through the Copilot coding agent in GitHub Actions needs additional build dependencies installed before it can run tests. Where should you define this setup?
Answer: D
Explanation:
copilot-setup-steps.yml defines a custom setup workflow (e.g., installing dependencies, configuring environment variables) that runs before the coding agent starts its task, ensuring it has the environment it needs.
NEW QUESTION # 83
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
The two ways repository-level memories will be deleted or removed from a repository are automatic time-based expiration and manual deletion by a repository administrator.
1. Automatic 28-Day Expiration
GitHub Copilot Memory utilizes an automated lifecycle policy to prevent stale knowledge from remaining in your codebase indefinitely. Any repository-level facts captured by Copilot will automatically expire and be deleted after 28 days if they are not actively updated or verified against the codebase.
2. Manual Admin Deletion
Repository administrators can manually curate and erase stored memories directly from the GitHub interface.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/copilot-memory/manage-as-administrator
NEW QUESTION # 84
You are evaluating how agent1 will behave after you implement the planned changes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 85
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You are monitoring the agents from the terminal by using the GitHub CLI.
An agent appears stalled.
You need to live stream the session log output.
What should you do?
Answer: D
Explanation:
To live stream the session log output of a background agent, you must run the gh agent-task view
<task-id> --log --follow command.
Command Syntax
gh agent-task view <task-id> --log --follow
Parameter Breakdown
<task-id>: The unique identifier of the stalled agent task. If you do not know the ID, run gh agent- task list first to look it up.
--log: Instructs the GitHub CLI to pull the specific execution and reasoning logs rather than just a summary of the task state.
--follow: Tells the CLI to keep the connection open and live stream new log events to your terminal in real-time as they happen.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-on-github/use-copilot-agents/manage-and-track-agents
NEW QUESTION # 86
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 issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?
Answer: B
Explanation:
Scenario: Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
To avoid race conditions and conflicting outputs when multiple Copilot agents modify the same shared/config.yaml file in parallel, the best practice is to configure a concurrency group on both agent workflows so that only one workflow runs at a time.
Workflow Concurrency
Using a concurrency group locks the shared resource. If one Copilot agent is already processing and modifying shared/config.yaml, the second agent's workflow will either queue or be canceled, preventing concurrent writes and eliminating file overwrites.
Incorrect:
Branch isolation is useful for developer edits, but in automated parallel agent operations, it still leads to frequent and painful merge conflicts if both agents make asynchronous structural updates to the same config file.
Reference:
https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency
NEW QUESTION # 87
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