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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	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	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 4	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 (Q37-Q42):

NEW QUESTION # 37
How does the /tests slash command assist developers?

A. Creates unit tests for the selected code.
B. Integrates with external testing frameworks.
C. Executes test cases to find issues with the code.
D. Constructs detailed test documentation.

Answer: A

Explanation:
The /tests slash command in GitHub Copilot Chat creates unit tests for the selected code, helping developers ensure the functionality and reliability of their code.

NEW QUESTION # 38
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. Enforce blocking of MIT or GPL licensed code
C. You need to check code suggestions yourself before accepting
D. Suggestions matching public code to 'blocked'

Answer: D

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 # 39
How do you generate code suggestions with GitHub Copilot in the CLI?

A. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
B. Use copilot suggest -> Write the command you want -> 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. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.

Answer: A

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 # 40
What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution.
Choose two.)

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

Answer: C,D

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 # 41
What is a likely effect of GitHub Copilot being trained on commonly used code patterns?

A. Suggest code snippets that reflect the most common practices in the training data.
B. Suggest innovative coding solutions that are not yet popular.
C. Suggest homogeneous solutions if provided a diverse data set.
D. Suggest completely novel projects, while reducing time on a project.

Answer: A

Explanation:
GitHub Copilot is likely to suggest code snippets that reflect common practices in its training data, as it learns from frequently used patterns.

NEW QUESTION # 42
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Reliable GitHub-Copilot Exam Testking | GitHub-Copilot Examcollection Questions Answers

GitHub-Copilot Examcollection Questions Answers - Valid GitHub-Copilot Dumps Demo

GitHub GitHub-Copilot Exam Syllabus Topics:

GitHub CopilotCertification Exam Sample Questions (Q37-Q42):

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