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GitHub GitHub-Copilot Exam Overview:

Certification Vendor:	GitHub, Microsoft
Exam Name:	GitHub Copilot Certification Exam
Exam Number:	GH-300
Available Languages:	Portuguese (Brazil), Spanish, English, Japanese, Korean
Exam Price:	$99 USD
Certificate Validity Period:	24 months
Real Exam Qty:	60–65
Exam Format:	Scenario-based, Multi-select, Multiple-choice, Case study
Exam Duration:	100 minutes
Passing Score:	700 / 1000 (approx. 70%)
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	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	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 (Q59-Q64):

NEW QUESTION # 59
How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?

A. GitHub Copilot rejects all prompts involving deprecated features to avoid compilation errors.
B. GitHub Copilot may suggest deprecated syntax or features if they are present in its training data.
C. GitHub Copilot always filters out deprecated elements to promote the use of current standards.
D. GitHub Copilot automatically updates deprecated features in its suggestions to the latest version.

Answer: B

Explanation:
GitHub Copilot generates suggestions from patterns in its training corpus, so if deprecated syntax or features were part of that data, it can still propose them-developers must review and update any outdated code.

NEW QUESTION # 60
What can be done during AI development to minimize bias?

A. Improve on the computational efficiency and speed.
B. Use diverse data, fairness metrics, and human oversight.
C. Collect massive amounts of data for training.
D. Focus on accuracy of the data.

Answer: B

Explanation:
Incorporating diverse datasets, applying fairness metrics throughout development, and maintaining human oversight are proven strategies for detecting and mitigating bias in AI systems.

NEW QUESTION # 61
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?

A. The explanations are primarily derived from user-provided documentation.
B. Reviewing and validating the generated output for accuracy and completeness.
C. The explanations are dynamically updated based on user feedback.
D. GitHub Copilot Chat uses a static database for generating explanations.

Answer: B

Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.

NEW QUESTION # 62
What role does chat history play in GitHub Copilot's code suggestions?

A. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.
B. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.
C. Chat history is used to train the GitHub Copilot model in real-time.
D. Chat history is stored and shared with other users to enhance collaboration.

Answer: A

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 # 63
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?

A. Enable GitHub Copilot license checking
B. Suggestions matching public code to 'blocked'
C. You need to check code suggestions yourself before accepting
D. Enforce blocking of MIT or GPL licensed code

Answer: B

Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.

NEW QUESTION # 64
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