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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.
Topic 3	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 4	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.
Topic 5	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 6	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.

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GitHub CopilotCertification Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
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.

Answer: A,D

Explanation:
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.

NEW QUESTION # 57
When using GitHub Copilot Chat to generate boilerplate code for various test types, how can you guide the AI to follow the testing standards of your company?

A. By using a specific slash command in the prompt.
B. By using specific prompt examples in your chat request.
C. By using a specific setting in GitHub Copilot's configuration.
D. By using a specific command in the terminal.

Answer: B

Explanation:
Providing concrete examples of your company's test structure and standards within the prompt steers Copilot Chat to generate boilerplate that aligns with your guidelines.

NEW QUESTION # 58
What method can be used to interact with GitHub Copilot?

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

Answer: C

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).

NEW QUESTION # 59
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)

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

Answer: A,B

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

NEW QUESTION # 60
As a developer who is evaluating the efficiency of GitHub Copilot in generating accurate code across different problem domains. During your R&D, you want to consider the limitations of GenAI tools like Copilot. Which of the following options best describes a core limitation?

A. Copilot reduces the need for STLC and code reviews
B. Copilot can only be used offline without internet access
C. Copilot may return output that reflects bias or inaccurate info because of its training dataright
D. Copilot always generates error-free code across all languages

Answer: C

Explanation:
Copilot is trained on a vast amount of data, which is publicly available; this data may include the biased, outdated, or incorrect practices of code implementation. This typically highlights a key concern in Responsible AI.
References:
https://learn.microsoft.com/en-us/training/modules/responsible-ai-with-github-Copilot/2-manage-ai-risks

NEW QUESTION # 61
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