Free PDF Quiz 2025 GitHub-Copilot: GitHub CopilotCertification Exam–Professional Pass Exam

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	How GitHub Copilot Works and Handles DataThis section of the exam measures the skills of Data Security Specialists and DevOps Engineers and covers how GitHub Copilot processes data, handles code suggestions and manages privacy concerns. It explains the data pipeline for Copilot’s suggestions, how it gathers context, and how prompts are processed through its AI model. The section also discusses the limitations of AI-generated code, the effects of historical data on suggestions, and the role of prompt crafting. Best practices for improving prompt effectiveness and optimizing AI-generated responses are included.
Topic 2	Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.
Topic 3	Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.
Topic 4	Privacy Fundamentals and Context Exclusions: This section of the exam measures skills of Cybersecurity Specialists and Compliance Officers and covers privacy safeguards and content exclusion settings in GitHub Copilot. It explains how Copilot can identify security vulnerabilities, suggest optimizations, and enforce secure coding practices. It also includes details on content ownership, data filtering mechanisms, and exclusion configurations. The section concludes with troubleshooting guidelines for managing context exclusions and ensuring compliance with organizational security policies.
Topic 5	Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.
Topic 6	Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.

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GitHub CopilotCertification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
What method can be used to interact with GitHub Copilot?

A. By using chat capabilities in NeoVim
B. By using a properly configured GitHub CLI
C. From a web browser at https://github.copilot.com
D. From a watch window in an IDE debug session

Answer: A

Explanation:
GitHub Copilot is an AI-powered code completion tool that integrates directly into supported Integrated Development Environments (IDEs) and code editors, such as Visual Studio Code, JetBrains IDEs, NeoVim, and others. Developers interact with it through their coding environment, where it provides real-time code suggestions, autocompletions, and (in some cases) chat-like capabilities via extensions or plugins (e.g., GitHub Copilot Chat in supported editors).
Evaluation of Options:
* A. By using a properly configured GitHub CLIThe GitHub CLI (Command Line Interface) is a tool for interacting with GitHub repositories and workflows from the terminal, but it is not a method for interacting with GitHub Copilot. Copilot operates within code editors/IDEs, not through the CLI.
Incorrect.
* B. By using chat capabilities in NeoVimThis is partially correct. GitHub Copilot can be used in NeoVim with the appropriate plugin (e.g., the Copilot.vim plugin), and GitHub Copilot Chat-a feature that allowsconversational interaction-may also be available depending on the setup and version.
However, "chat capabilities in NeoVim" alone is not the primary or standard way to describe Copilot interaction, as it's more about code suggestions than chat. This is the closest option but not perfectly precise.Partially correct.
* C. From a watch window in an IDE debug sessionThe "watch window" in an IDE is used during debugging to monitor variable values, not to interact with GitHub Copilot. Copilot provides suggestions while coding, not specifically in debug sessions or watch windows.Incorrect.
* D. From a web browser athttps://github.copilot.comThere is no such URL as "https://github.copilot.
com" dedicated to interacting with GitHub Copilot. Copilot is accessed via GitHub's authentication and integrated into editors/IDEs, not through a standalone web browser interface. Information about Copilot is available on GitHub's official site (e.g.,https://github.com/features/copilot), but interaction happens in the coding environment.Incorrect.

NEW QUESTION # 41
How can GitHub Copilot be limited when it comes to suggesting unit tests?

A. GitHub Copilot's limitations in generating unit tests can vary based on the IDE version you are using.
B. GitHub Copilot primarily suggests basic unit tests that focus on core functionalities, often requiring additional input from developers for comprehensive coverage.
C. GitHub Copilot can handle any complexity in code and automatically generate appropriate unit tests.
D. GitHub Copilot can generate all types of unit tests, including those for edge cases and complex integration scenarios.

Answer: B

Explanation:
GitHub Copilot often suggests basic unit tests and may not cover all edge cases or complex integration scenarios, requiring developers to supplement its suggestions.

NEW QUESTION # 42
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)

A. Explain a legacy COBOL code and translate the code to another language like Python.
B. Creation of end-to-end performance testing scenarios for a web application
C. Creation of a unit test scenario for newly developed Python code
D. Create a technical requirement specification from the business requirement documentation

Answer: A,C

Explanation:
GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.

NEW QUESTION # 43
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

A. By highlighting the code you want to fix, right-clicking, and selecting "Refactor using GitHub Copilot."
B. By adding comments to your code and triggering a suggestion.
C. By using the "/fix" command in GitHub Copilot in-line chat.
D. By running the gh copilot fix command.
E. By highlighting the code you want to fix, right-clicking, and selecting "Fix using GitHub Copilot."

Answer: A,B

Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting "Refactor using GitHub Copilot" from the context menu.

NEW QUESTION # 44
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

A. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.
B. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.
C. GitHub Copilot can independently verify the statistical significance of results.
D. GitHub Copilot can design new statistical methods that have not been previously documented.

Answer: B

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.

NEW QUESTION # 45
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