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| Section | Weight | Objectives |
|---|---|---|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Design agentic AI solutions | 25% | - Design agent architecture
|
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NEW QUESTION # 85
You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
You discover that some workflow run artifacts are being deleted manually.
You need to use your organization's audit log data to identify which user deleted the artifacts.
Which audit log search filter should you use?
Answer: D
Explanation:
The filter action:artifact.destroy selects the audit event associated with manual deletion of a workflow artifact. It directly targets the activity described in the scenario, allowing the investigator to examine the recorded actor and associated event details.
A repository filter narrows the search to a particular repository but does not distinguish artifact deletion from other recorded operations. It can usefully accompany the action filter when investigating a specific project. A workflow-run action concerns workflow execution rather than the manual removal of its stored artifacts. The generic operation:remove option does not select the documented artifact deletion event.
The investigation should correlate the deletion event with its timestamp and repository information, then inspect the actor recorded for that event. This identifies the account associated with the action; additional context may be needed when an application or automated identity performed it.
Artifact retention and audit logging serve separate purposes. Retention controls how long outputs are normally preserved, while the audit log records relevant administrative and user activity. Searching the audit log does not restore deleted content.
Study-guide topics: accountability, evidence preservation, actor attribution, and audit trails. Reference: GitHub-Organization audit log events.
NEW QUESTION # 86
You have a GitHub Copilot coding agent named Orchestrator that runs a multi-phase workflow by using the following subagents:
- Explorer gathers context by using read-only tools.
- Modifier applies focused edits.
You are adding a new agent named Summarizer that generates a concise summary after modifications are complete. Summarizer includes the following YAML frontmatter.
The Orchestrator agent lists all three agents in its agents property.
After adding the Summarizer agent, Orchestrator successfully runs Explorer and Modifier but fails to run Summarizer.
What is a possible cause of the failure?
Answer: D
Explanation:
The primary reason for this failure is the disable-model-invocation: true setting in the Summarizer's YAML frontmatter.In the GitHub Copilot Agent configuration framework, when an orchestrator agent automates a multi-agent workflow, it relies on the base LLM model to agentically trigger and delegate tasks to its subagents.
Blocks Subagent Delegation: Setting disable-model-invocation: true instructs GitHub Copilot to completely prevent the model from automatically invoking or calling this agent as a subagent.
Requires Manual Intervention: When this property is true, the agent can only be triggered via a direct manual request by the user (such as explicitly picking it from a chat menu or a slash command). Because user-invocable is also set to false, it becomes completely unreachable in this workflow.
Contradicts Orchestration: Even though Orchestrator explicitly registers Summarizer in its agents property, the underlying model respects the disable-model-invocation: true safety/routing block and refuses to spin up the subagent loop for it.
Reference:
https://docs.github.com/en/copilot/how-tos/copilot-sdk/features/custom-agents
NEW QUESTION # 87
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: A
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 # 88
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: A
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 # 89
Drag and Drop Question
You have a GitHub repository that uses GitHub Actions for CI on pull requests.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
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:
Explanation:
Box 1: npm test
npm test (or mvn test, pytest, dotnet test depending on your language stack).
The run keyword requires a shell command to execute your test runner.
Box 2: init
The CodeQL workflow must initialize its database and environment before building code. The official action for this is github/codeql-action/init.
Box 3: analyze
After the code is prepared (and built if using a compiled language), the final step scans the codebase and uploads the SARIF results to GitHub Security. The official action is github/codeql- action/analyze.
Reference:
https://gitprotect.io/blog/github-actions-how-to-use-it-for-security-and-compliance-needs/
NEW QUESTION # 90
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