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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
Passing Score:	700 / 1000 (approx. 70%)
Exam Price:	$99 USD
Certificate Validity Period:	24 months
Exam Format:	Case study, Scenario-based, Multiple-choice, Multi-select
Available Languages:	Portuguese (Brazil), Japanese, English, Korean, Spanish
Real Exam Qty:	60–65
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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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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	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 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.

GitHub CopilotCertification Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
Which of the following describes role prompting?

A. Tell GitHub Copilot in what tone of voice it should respond
B. Prompt GitHub Copilot to explain what was the role of a suggestion
C. Giving GitHub Copilot multiple examples of the form of the data you want to use
D. Describing in your prompt what your role is to get a better suggestion

Answer: D

Explanation:
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.

NEW QUESTION # 124
When using an IDE with a supported GitHub Copilot plug-in, which Chat features can be accessed from within the IDE? Each correct answer presents part of the solution. (Choose two.)

A. Explain code and suggest improvements
B. Find out about releases and commits
C. Plan coding tasks
D. Generate unit tests

Answer: A,D

Explanation:
Within your IDE's Copilot Chat pane you can ask it to explain existing code and suggest improvements.
You can also prompt it to generate unit tests for your code directly in the editor.

NEW QUESTION # 125
GitHub Copilot in the Command Line Interface (CLI) can be used to configure the following settings: (Each correct answer presents part of the solution. Choose two.)

A. The default editor
B. GitHub CLI subcommands
C. The default execution confirmation
D. Usage analytics

Answer: C,D

Explanation:
GitHub Copilot in the CLI allows configuration of settings such as the default execution confirmation and usage analytics. These settings help tailor the CLI experience to the user's preferences.

NEW QUESTION # 126
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. Explain a legacy COBOL code and translate the code to another language like Python.
C. Create a technical requirement specification from the business requirement documentation
D. Creation of a unit test scenario for newly developed Python code

Answer: B,D

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

NEW QUESTION # 127
How can the concept of fairness be integrated into the process of operating an AI tool?

A. Training AI data and algorithms to be free from biases will ensure fairness.
B. Focusing on accessibility will ensure fairness.
C. Regularly monitoring the AI tool's performance will ensure fairness in its outputs.
D. Focusing on collecting large datasets for training will ensure fairness.

Answer: A

Explanation:
Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.

NEW QUESTION # 128
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