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GitHub-Copilot Exam Questions
Exam Name: GitHub Copilot Certification Exam
Exam Code:
GitHub-Copilot
Related Certification(s): GitHub Certifications
Certification Provider: GitHub
Number of GitHub-Copilot practice questions in our database:
65 (updated: Apr. 03, 2025)
Expected GitHub-Copilot Exam Topics, as suggested by GitHub :
- Topic 1: 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 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: 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 4: 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 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.
- Topic 7: 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.
Disscuss GitHub GitHub-Copilot Topics, Questions or Ask Anything Related
Free GitHub GitHub-Copilot Exam Actual Questions
Note: Premium Questions for GitHub-Copilot were last updated On Apr. 03, 2025 (see below)
Question #1
effectiveness of GitHub Copilot? (Each correct answer presents part of the solution. Choose two.) 1
1.
blog.yatricloud.com
blog.yatricloud.com
Correct Answer:
B, E
The GitHub Copilot usage metrics API provides insights into the acceptance rate of code suggestions and Copilot Chat specific suggestions acceptance metrics, helping organizations evaluate its effectiveness.
Question #2
How does GitHub Copilot suggest code optimizations for improved performance?
Correct Answer:
A
GitHub Copilot suggests code optimizations by analyzing the codebase and recommending more efficient algorithms or data structures.
Question #3
Which GitHub Copilot plan allows for prompt and suggestion collection?
Correct Answer:
C
GitHub Copilot Enterprise allows for prompt and suggestion collection, enabling organizations to analyze and improve their usage of the tool.
Question #4
How does GitHub Copilot Enterprise assist in code reviews during the pull request process? (Select two.)
Correct Answer:
B, D
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.
Question #5
Which of the following statements best describes the impact of GitHub Copilot on the software development process?
Correct Answer:
C
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
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