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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:GitHub, Microsoft
Exam Name:GitHub Copilot Certification Exam
Exam Number:GH-300
Certificate Validity Period:24 months
Exam Duration:100 minutes
Passing Score:700 / 1000 (approx. 70%)
Real Exam Qty:60–65
Exam Format:Multiple-choice, Scenario-based, Multi-select, Case study
Exam Price:$99 USD
Available Languages:Portuguese (Brazil), Japanese, Spanish, English, Korean
Recommended Training:GitHub Copilot Learning Path
GitHub Official Documentation
Exam Registration:Official GitHub Certification Page
Pearson VUE Registration
Sample Questions:GitHub GitHub-Copilot Sample Questions
Exam Way:Online proctored or in-person at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended: foundational GitHub knowledge, experience with at least one programming language, hands-on use of Copilot
Official Syllabus URL:https://learn.github.com/certification/COPILOT

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Exam Questions for GitHub GitHub-Copilot in PDF Format

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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • How GitHub Copilot Works and Handles Data: This 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 3
  • 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 4
  • 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 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
  • 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.

GitHub CopilotCertification Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
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?

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 # 86
What role does chat history play in GitHub Copilot's code suggestions?

Answer: D

Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.


NEW QUESTION # 87
How does GitHub Copilot Enterprise assist in code reviews during the pull request process? (Select two.)

Answer: A,D

Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.


NEW QUESTION # 88
What kind of insights can the GitHub Copilot usage metrics API provide to help evaluate the effectiveness of GitHub Copilot? Each correct answer presents part of the solution. (Choose two.)

Answer: C,D

Explanation:
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.


NEW QUESTION # 89
What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

Answer: B,D,E

Explanation:
The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.


NEW QUESTION # 90
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