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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:	GitHub, Microsoft
Exam Name:	GitHub Copilot Certification Exam
Exam Number:	GH-300
Exam Duration:	100 minutes
Real Exam Qty:	60–65
Exam Price:	$99 USD
Available Languages:	Spanish, Japanese, Portuguese (Brazil), English, Korean
Passing Score:	700 / 1000 (approx. 70%)
Certificate Validity Period:	24 months
Exam Format:	Multi-select, Case study, Scenario-based, Multiple-choice
Recommended Training:	GitHub Copilot Learning Path GitHub Official Documentation
Exam Registration:	Pearson VUE Registration Official GitHub Certification Page
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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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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 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.

GitHub CopilotCertification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
What is zero-shot prompting?

A. Giving GitHub Copilot examples of the algorithm and outcome you want to use
B. Giving as little context to GitHub Copilot as possible
C. Only giving GitHub Copilot a question as a prompt and no examples
D. Telling GitHub Copilot it needs to show only the correct answer
E. Giving GitHub Copilot examples of the problem you want to solve

Answer: C

Explanation:
Zero-shot prompting involves asking GitHub Copilot a question or giving a task without providing any examples. This relies on the model's pre-existing knowledge to generate a response.

NEW QUESTION # 63
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. Chat
C. Pull Request Diff Analysis
D. Knowledge Base

Answer: A,B

NEW QUESTION # 64
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 can design new statistical methods that have not been previously documented.
C. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.
D. GitHub Copilot can independently verify the statistical significance of results.

Answer: C

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 # 65
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot Chat can provide guidance and support for common coding tasks and challenges in the targeted programming language.
B. GitHub Copilot can provide contextualized code suggestions and answer sources from an organization's documentation.
C. GitHub Copilot's /understand command will help GitHub Copilot to understand code written in a targeted programming language.
D. GitHub Copilot can convert comments into code to grasp the syntax and nuances of a new programming language.

Answer: A,D

Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.

NEW QUESTION # 66
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 end-to-end performance testing scenarios for a web application
B. Create a technical requirement specification from the business requirement documentation
C. Creation of a unit test scenario for newly developed Python code
D. Explain a legacy COBOL code and translate the code to another language like Python.

Answer: C,D

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

NEW QUESTION # 67
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