GitHub-Copilot New Learning Materials - GitHub-Copilot Exam Objectives Pdf

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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	Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.
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	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 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.
Topic 6	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.

GitHub CopilotCertification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
What is a benefit of using custom models in GitHub Copilot?

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

Answer: A

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 # 35
Which of the following describes role prompting?

A. Tell GitHub Copilot in what tone of voice it should respond
B. Describing in your prompt what your role is to get a better suggestion
C. Prompt GitHub Copilot to explain what was the role of a suggestion
D. Giving GitHub Copilot multiple examples of the form of the data you want to use

Answer: B

Explanation:
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.

NEW QUESTION # 36
What are the potential risks associated with relying heavily on code generated from GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. GitHub Copilot may decrease developer velocity by requiring too much time in prompt engineering.
B. GitHub Copilot may introduce security vulnerabilities by suggesting code with known exploits.
C. GitHub Copilot's suggestions may not always reflect best practices or the latest coding standards.
D. GitHub Copilot may increase development lead time by providing irrelevant suggestions.

Answer: B,C

Explanation:
Heavy reliance on GitHub Copilot can introduce security vulnerabilities if the generated code contains known exploits. Additionally, Copilot's suggestions may not always align with best practices or the latest standards, requiring careful review and validation.

NEW QUESTION # 37
How long does it take content exclusion to add or be updated?

A. Up to 30 minutes
B. 24 hours
C. 45 - 60 minutes
D. 60 - 90 minutes

Answer: A

Explanation:
Content exclusions typically take up to 30 minutes to be added or updated.

NEW QUESTION # 38
Which of the following scenarios best describes the intended use of GitHub Copilot Chat as a tool?

A. A solution for software development, requiring no additional input or oversight.
B. A complete replacement for developers generating code.
C. A tool solely designed for debugging and error correction.
D. A productivity tool that provides suggestions, but relying on human judgment.

Answer: D

Explanation:
GitHub Copilot Chat is designed to be a productivity enhancer, not a replacement for human developers. It provides suggestions and assists with coding tasks, but the final decision and validation always rest with the developer. Copilot Chat is meant to augment the developer's workflow, making it faster and more efficient, but it does not remove the need for human oversight and judgment.

NEW QUESTION # 39
......

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GitHub-Copilot New Learning Materials - GitHub-Copilot Exam Objectives Pdf

GitHub-Copilot Exam Objectives Pdf | GitHub-Copilot Exam Testking

GitHub GitHub-Copilot Exam Syllabus Topics:

GitHub CopilotCertification Exam Sample Questions (Q34-Q39):

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