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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	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	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	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.

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GitHub CopilotCertification Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
How can GitHub Copilot assist with code refactoring tasks?

A. GitHub Copilot can remove unnecessary files from the project directory.
B. GitHub Copilot can suggest refactoring improvements for better code quality.
C. GitHub Copilot can fix syntax errors without user input.
D. GitHub Copilot can automatically rewrite code to follow best practices.

Answer: B

Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.

NEW QUESTION # 36
What is a benefit of using custom models in GitHub Copilot?

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

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 # 37
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

A. It primarily relies on the model's training data to generate responses.
B. It uses user-provided documentation exclusively to generate responses.
C. It primarily uses search results from Bing to generate responses.
D. It combines its training data set, code in user repositories, and external sources like Bing to generate responses.

Answer: D

Explanation:
GitHub Copilot Chat combines its training data, code from user repositories, and external sources like Bing to generate comprehensive and relevant responses to coding questions.

NEW QUESTION # 38
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 completely novel projects, while reducing time on a project.
C. Suggest innovative coding solutions that are not yet popular.
D. Suggest homogeneous solutions if provided a diverse data set.

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 # 39
When can GitHub Copilot still use content that was excluded using content exclusion?

A. If the contents of an excluded file are referenced in code that is not excluded, for example function calls.
B. If the content exclusion was configured at the enterprise level, and is overwritten at the organization level.
C. When the repository level settings allow overrides by the user.
D. When the user prompts with @workspace.

Answer: A

Explanation:
GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.

NEW QUESTION # 40
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Latest GitHub-Copilot Learning Material & GitHub-Copilot Free Braindumps

GitHub GitHub-Copilot Exam Syllabus Topics:

Latest GitHub-Copilot Learning Material - Pass Guaranteed Quiz 2025 First-grade GitHub-Copilot: GitHub CopilotCertification Exam Free Braindumps

GitHub CopilotCertification Exam Sample Questions (Q35-Q40):

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