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GitHub-Copilot New Learning Materials - 100% First-grade Questions Pool

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 DataThis 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

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 5

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.

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GitHub CopilotCertification Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which of the following is correct about GitHub Copilot Knowledge Bases?

A. Indexing is static
B. All file types are indexed
C. All repos are indexed
D. It is an Enterprise feature

Answer: D

Explanation:
GitHub Copilot Knowledge Bases is an Enterprise feature that allows the tool to use an organization's internal documentation and code for more accurate and relevant suggestions.

NEW QUESTION # 46
When can GitHub Copilot still use content that was excluded using content exclusion?

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

Answer: B

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

NEW QUESTION # 47
How does GitHub Copilot Chat help in understanding the existing codebase?

A. By providing visual diagrams of the code structure.
B. By automatically refactoring code to improve readability.
C. By running code linters and formatters.
D. By answering questions about the code and generating explanations.

Answer: D

Explanation:
GitHub Copilot Chat helps in understanding existing codebases by answering questions about the code and generating explanations. This allows developers to quickly grasp the functionality and structure of unfamiliar code.

NEW QUESTION # 48
How does GitHub Copilot Chat ensure that a function works correctly?

A. By executing the test cases to validate the correctness of the code.
B. By suggesting assertions based on the code's context and semantics.
C. By automatically writing all the tests for the function.
D. By writing the implementation code for the function.

Answer: B

Explanation:
GitHub Copilot Chat can suggest assertions based on the code's context and semantics to help developers verify the correctness of their functions. These assertions serve as checks that the function behaves as expected under various conditions.

NEW QUESTION # 49
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

A. Provide insight on where to get the content from to get a response
B. Provide all information about the utilized files
C. Provide specific success criteria
D. Provide links to supporting documentation

Answer: C,D

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.

NEW QUESTION # 50
......

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