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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Topic 2: Implement agents and multi-agent systems | 30% | - Orchestrate multi-agent collaboration
|
| Topic 3: Design agentic AI solutions | 25% | - Design agent architecture
|
| Topic 4: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
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NEW QUESTION # 62
You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated security scanning signals available for the pull request? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
CodeQL findings and secret-detection results are the intended selections. CodeQL analyzes code for patterns associated with vulnerabilities and programming errors. Secret scanning identifies sensitive material, including supported credential and token patterns, that may have been introduced into the generated changes.
Both provide concrete security evidence that a reviewer can evaluate alongside the proposed fix.
Linting generally evaluates coding conventions and selected correctness rules; a generic linting failure is not necessarily a security finding. Median time to merge measures delivery performance rather than the security of a particular change.
The wording of option D creates an important technical distinction. Dependency vulnerability detection is also a genuine automated security signal. GitHub explicitly documents dependency analysis alongside CodeQL and secret scanning in the coding agent's security checks. Repository-level dependency alerts, however, may concern existing vulnerabilities rather than changes introduced by this pull request. B and C are the clearest intended pair, but D cannot be dismissed as unrelated to security.
Reviewers should determine whether each finding applies to the proposed changes and whether the underlying vulnerability has actually been resolved.
Relevant curriculum topics are automated evaluation signals, security scanning, and evidence-based assessment of agent outputs.
Reference: Copilot coding agent security and quality validation .
NEW QUESTION # 63
You need to enable Copilot memory to support the planned changes.
What should you configure?
Answer: A
Explanation:
Copilot memory is an organization-level capability. Configuring it through the organization's Copilot settings establishes the governance boundary, availability, and policy controls consistently for the developers and repositories covered by that organization.
Personal settings are unsuitable because they create inconsistent behavior between developers and do not provide centralized administration. A repository-level configuration can supply repository instructions and scoped context, but it does not replace the organization-level control that enables and governs the memory capability. An individual agent profile defines role behavior, tool access, prompts, and delegation settings; it is not the tenant-level location for enabling Copilot memory.
Organization configuration is particularly important when agents must retain approved context, recurring preferences, and workflow guidance across sessions without each developer configuring behavior independently. Central governance also allows administrators to maintain predictable controls over which organizational information is available to Copilot.
Study-guide topics: organizational governance, persistent context, and centralized agent configuration.
NEW QUESTION # 64
You want the GitHub Copilot coding agent to follow project-specific conventions (coding style, testing requirements, folder structure) on every task it performs in a repository. What should you create?
Answer: A
Explanation:
The correct configuration is .github/copilot-instructions.md. GitHub defines this file as the repository-wide custom-instructions mechanism for providing persistent project-specific guidance to Copilot. Instructions stored there are automatically incorporated when Copilot works in the repository, making the file appropriate for conventions that should apply repeatedly, such as coding standards, architectural expectations, preferred test frameworks, build and validation commands, repository layout, and required implementation patterns.
This is particularly important for a coding agent because the instructions establish persistent SDLC context rather than relying on developers to repeat requirements in every issue or prompt. GitHub explicitly describes repository instructions as a way to tell Copilot how to understand, build, test, and validate repository changes.
CODEOWNERS controls ownership and review assignment rather than Copilot behavior. A .copilotignore file is not the repository-wide custom-instructions mechanism, and agents.yml is not the prescribed file for persistent repository conventions.
Study Guide Reference Topics: Prepare agent architecture and SDLC processes; repository-level agent instructions; persistent development conventions; automated build and test guidance.
NEW QUESTION # 65
Drag and Drop Question
Your company uses GitHub Copilot Enterprise.
Developers use GitHub Copilot agent mode in Microsoft Visual Studio Code on their laptops and Copilot Chat on github.com when they are away from their laptops.
When switching between environments, the developers notice that agent workflows lose continuity because the tools available in Visual Studio Code are unavailable on github.com.
You need to ensure that the agent tools and state are available consistently across environments and can be used from any device without local setup.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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: Use a Github-hosted Model Context Protocol (MCP) server for cloud-based workflows.
To ensure tools and states remain identical whether developers are on local laptops or on github.com (or GitHub Mobile), you must deploy Copilot Cloud Agents paired with remote cloud sessions.
Utilize Cloud Sessions: Shift developers away from local-only agent workflows. Cloud sessions run agents inside isolated, GitHub-hosted cloud environments rather than relying on local machine resources. This permits developers to resume, steer, and monitor active workflows from any device using the browser or the GitHub Copilot App.
Box 2: Enable MCP servers in Copilot policy
To ensure that agent tools and states are available consistently across all environments (including github.com) and can be used from any device without local setup, you must Enable MCP servers in Copilot policy.
Centralized Enforcement: Enabling the Model Context Protocol (MCP) policy at the enterprise or organization level allows administrators to define a central registry of approved tools.
Cross-Environment Continuity: Once the policy is active and servers are configured in the repository or organization settings, the cloud-based Copilot environment (github.com) natively inherits those exact tools. This removes dependency on individual laptop configurations or local extension states.
Reference:
https://docs.github.com/en/copilot/how-tos/administer-copilot/manage-for-enterprise/manage-agents/enable-copilot-cloud-agent
https://docs.github.com/en/copilot/how-tos/provide-context/use-mcp-in-your-ide/extend-copilot-chat-with-mcp
NEW QUESTION # 66
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: D
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 # 67
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