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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Design agentic AI solutions | 25% | - Design agent architecture
|
| Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
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NEW QUESTION # 102
You have a GitHub Actions workflow that starts multiple agent jobs in parallel.
You need to prevent two runs in the same job from executing simultaneously on the same branch. The solution must ensure, at most, one running instance and one pending instance per branch.
What should you add to the job?
Answer: A
Explanation:
A job-level concurrency group coordinates executions that share the same group value across workflow runs. The expression ci-${{ github.ref }} places executions for the same ref into the same group while allowing other refs to use independent groups.
Under the default concurrency behavior, a group permits at most one running execution and one pending execution. When another execution enters an occupied group, it becomes pending; an existing pending execution can be replaced by the newer one. This is a concurrency limit rather than a guarantee that every queued execution will eventually run.
The strategy.max-parallel setting controls simultaneous jobs generated by a matrix within a workflow run. It does not establish the required coordination between separate workflow runs. A matrix definition also does not create a shared concurrency lock. The cancel-in-progress property controls whether an active execution is cancelled, but a cancellation expression alone does not define the necessary group.
Group names should be chosen carefully when multiple jobs or workflows share a repository, because identical names can coordinate those executions too.
Study-guide topics: job synchronization, concurrency groups, and bounded execution. Reference: GitHub Actions-Workflow syntax: job concurrency.
NEW QUESTION # 103
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: D
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 # 104
You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?
Answer: C
Explanation:
The repository-wide Copilot instruction file is .github/copilot-instructions.md. GitHub explicitly identifies this file as the location for review guidance that should apply throughout the codebase. It can describe coding standards, security expectations, error-handling requirements, and other review criteria that should be considered across pull requests.
A pull request template primarily structures the description supplied when a pull request is created. It is not the designated repository-wide Copilot instruction mechanism. Files beneath .github/instructions support instructions with defined applicability, commonly using path patterns. The filename in option C alone does not establish repository-wide scope. Custom agent profiles define the behavior of particular agents and do not replace the standard configuration for Copilot code review.
The instructions should state concrete, reviewable requirements rather than vague pReference. For example, a rule about checking authorization at a defined service boundary is more actionable than a general instruction to "ensure security." Repository settings must also allow custom instructions to be used for code review.
This configuration improves consistency while human reviewers remain responsible for evaluating the resulting findings.
Relevant curriculum topics are tuning instructions, defining evaluation criteria, and aligning automated review with development intent.
Reference:
NEW QUESTION # 105
While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?
Answer: C
Explanation:
Agent mode can be granted permission to invoke terminal commands (like running a test suite) as part of its autonomous workflow, allowing it to self-validate changes rather than requiring the developer to run tests manually each time.
NEW QUESTION # 106
An enterprise administrator wants to audit which repositories have had Copilot coding agent- created pull requests over the last 30 days. Where should the administrator look?
Answer: C
Explanation:
GitHub's organization/enterprise audit log records agent activity, including pull requests created by the Copilot coding agent, giving administrators visibility for compliance and governance purposes.
NEW QUESTION # 107
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