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GitHub GitHub-Copilot Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Responsible AI	7%–20%	- Legal and compliance considerations - Ethical and safe use of AI tools
Topic 2: Privacy Fundamentals and Context Exclusions	15%–20%	- Configuring context exclusion rules - Protecting sensitive information
Topic 3: Testing with GitHub Copilot	9%–15%	- Verifying output quality and correctness - Generating tests and validation
Topic 4: Prompt Crafting and Prompt Engineering	9%–15%	- Designing effective prompts - Iterative improvement and context management
Topic 5: GitHub Copilot Plans and Features	25%–31%	- Free, Pro, Business, Enterprise plans - Feature comparison and capabilities - Configuration and settings
Topic 6: How GitHub Copilot Works and Handles Data	10%–15%	- Data processing, retention, and usage policies - Architecture and underlying technology - Context collection and privacy controls
Topic 7: Developer Use Cases for AI	14%–20%	- Integration with IDEs and workflows - Code generation and completion - Refactoring, documentation, and testing

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GitHub CopilotCertification Exam GitHub-Copilot Prüfungsfragen mit Lösungen (Q111-Q116):

111. Frage
What do you check when GitHub Copilot content exclusions are not working? Each correct answer presents part of the solution. (Choose two.)

A. If the content exclusion settings changed in the last 30 minutes or before that.
B. If the user is part of the content exclusion team that limits the use of content exclusions.
C. If GitHub Copilot can connect to the server selected in your user settings.
D. If the user is in an organization that has content exclusions configured.

Antwort: A,D

Begründung:
Verify that the user belongs to an organization (or enterprise) where content exclusion rules have actually been set up.
Check whether the exclusion policy was recently modified - changes can take up to 30ꢀminutes to propagate before they take effect.

112. Frage
You are configuring GitHub Copilot in the CLI for a smoother developer experience. Which of the following aliases can be set up to perform common actions like code suggestions and explanations?

A. "ghce" - GitHub Copilot Explainright
B. "ghshare" - Share Copilot responses with your colleagueswrong
C. "ghcs" - GitHub Copilot Suggestright
D. "ghrun" - Run Copilot commands in a build pipelinewrong
E. "ghinit" - Initialize Copilot CLI

Antwort: A,C

Begründung:
ghcs is a commonly configured alias for the GitHub Copilot CLI command to suggest code. It simplifies the user experience by allowing developers to quickly prompt Copilot from the terminal.
ghce stands for GitHub Copilot Explain and is another helpful alias that allows developers to request natural language explanations for code from the terminal.
Reference:
https://docs.github.com/en/Copilot/managing-Copilot/configure-personal-settings/configuring-github-Copilot-in-the-cli#setting-up-aliases

113. Frage
Why is it important to ensure the security of the code used in Generative AI (Gen AI) tools?

A. Ensuring code security prevents unauthorized access and potential data breaches.
B. Ensuring code security maintains the integrity of the AI system.
C. Ensuring code security supports the development of more advanced AI features.
D. Ensuring code security enables the AI system to handle larger datasets effectively.

Antwort: A

Begründung:
Securing the code in GenꢀAI tools is critical to prevent unauthorized access to the system or its data, which could lead to sensitive information leaks or full-scale data breaches.

114. Frage
Which Copilot Individual features are available when using a supported extension for Visual Studio, VS Code, or JetBrains IDEs? (Each correct answer presents part of the solution. Choose two.)

A. Code suggestions
B. Knowledge Base
C. Chat
D. Pull Request Diff Analysis

Antwort: A,C

Begründung:
GitHub Copilot Individual provides code suggestions and chat features when used with supported IDE extensions like Visual Studio, VS Code, and JetBrains IDEs.

115. Frage
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?

A. GitHub Copilot can refactor applications to align with upcoming standards.
B. GitHub Copilot can directly convert legacy applications into cloud-native architectures.
C. GitHub Copilot can create and deploy full-stack applications based on a single query.
D. GitHub Copilot can suggest modern programming patterns based on your code.

Antwort: D

Begründung:
GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.
* Why not A?GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn't autonomously perform the full conversion process, which involves architectural decisions and deployment steps beyond its scope.
* Why not C?While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve a complete, deployable solution.
* Why not D?GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn't inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the data it was trained on, not future standards that are yet to be defined.
Thus,Bis the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.

116. Frage
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