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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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	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.

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GitHub CopilotCertification Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
How do you generate code suggestions with GitHub Copilot in the CLI?

A. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.
B. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
C. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
D. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.

Answer: B

Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.

NEW QUESTION # 44
What is a benefit of using custom models in GitHub Copilot?

A. Responses use the organization's LLM engine
B. Responses are guaranteed to be correct
C. Responses use practices and patterns in your repositories
D. Responses are faster to produce and appear sooner

Answer: C

Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and practices within your repositories. This results in suggestions that are more aligned with your organization's coding standards and conventions, improving the relevance and accuracy of the generated code.

NEW QUESTION # 45
What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution.
Choose two.)

A. Limited training data
B. Ability to handle complex code structures
C. Extensive support for all programming languages
D. No biases in code suggestions

Answer: A,C

Explanation:
GitHub Copilot Chat has limitations such as limited training data, which can affect the accuracy of its suggestions, and it does not provide extensive support for all programming languages.

NEW QUESTION # 46
1.
blog.yatricloud.com
blog.yatricloud.com

A. The API can refactor your code to improve productivity.
B. The API can generate detailed reports on code quality improvements made by GitHub Copilot.
C. The API can provide feedback on coding style and standards compliance.
D. The API can track the acceptance rate of code suggestions accepted and used in the organization.
E. The API can provide Copilot Chat specific suggestions acceptance metrics.

Answer: D,E

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 # 47
What GitHub Copilot pricing plan gives you access to your company's knowledge bases?

A. GitHub Copilot Enterprise
B. GitHub Copilot Individual
C. GitHub Copilot Business
D. GitHub Copilot Professional

Answer: A

Explanation:
GitHub Copilot Enterprise provides access to your company's knowledge bases, enabling the tool to provide contextually relevant suggestions based on your organization's specific documentation and code.

NEW QUESTION # 48
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