Associate GitHub-Copilot Level Exam, Test GitHub-Copilot King

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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

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GitHub CopilotCertification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
How long does GitHub retain Copilot data for Business and Enterprise? (Each correct answer presents part of the solution. Choose two.)

A. Prompts and Suggestions: Not retained
B. Prompts and Suggestions: Retained for 28 days
C. User Engagement Data: Kept for One Year
D. User Engagement Data: Kept for Two Years

Answer: B,D

Explanation:
For GitHub Copilot Business and Enterprise, prompts and suggestions are retained for 28 days to provide context and improve the service. User engagement data, which includes usage patterns and interactions, is kept for two years. This data retention policy is designed to balance service improvement with user privacy.

NEW QUESTION # 61
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

A. The Gen AI tool is overloaded with too many requests at once.
B. The Gen AI tool is programmed with a focus on creativity over factual accuracy.
C. The training data might contain biases or inconsistencies.
D. The Gen AI tool is experiencing downtime and is not fully recovered.

Answer: C

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.

NEW QUESTION # 62
Which Copilot Individual features are available when using a supported extension for Visual Studio, VS Code, or JetBrains IDEs? (Each correct answer presents part of the solution. Choose two.)

A. Knowledge Base
B. Chat
C. Pull Request Diff Analysis
D. Code suggestions

Answer: B,D

Explanation:
GitHub Copilot Individual provides code suggestions and chat features when used with supported IDE extensions like Visual Studio, VS Code, and JetBrains IDEs.

NEW QUESTION # 63
How does GitHub Copilot suggest code optimizations for improved performance?

A. By providing detailed reports on the performance of the codebase.
B. By analyzing the codebase and suggesting more efficient algorithms or data structures.
C. By enforcing strict coding standards that ensure optimal performance.
D. By automatically rewriting the codebase to use more efficient code.

Answer: B

Explanation:
GitHub Copilot suggests code optimizations by analyzing the codebase and recommending more efficient algorithms or data structures.

NEW QUESTION # 64
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 primarily uses search results from Bing to generate responses.
C. It uses user-provided documentation exclusively 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 # 65
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