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GitHub GitHub-Copilot Answers Free - Latest GitHub-Copilot Learning Materials

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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

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 3

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 4

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.

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GitHub GitHub-Copilot Exam Questions - Pass With Confidence!

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GitHub CopilotCertification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which of the following scenarios best describes the intended use of GitHub Copilot Chat as a tool?

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

Answer: A

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 # 13
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

A. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.
B. GitHub Copilot can design new statistical methods that have not been previously documented.
C. GitHub Copilot can independently verify the statistical significance of results.
D. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.

Answer: D

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.

NEW QUESTION # 14
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

A. It combines its training data set, code in user repositories, and external sources like Bing 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 primarily relies on the model's training data to generate responses.

Answer: A

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 # 15
A company is currently storing code in Bitbucket and would like to use GitHub Copilot. Which GitHub Copilot plan will be most cost effective to allow them to manage users with their Identity Provider (e.g. Okta)?

A. GitHub Copilot Business for non-GHE customers
B. GitHub Copilot Teams
C. GitHub Copilot Enterprise
D. GitHub Copilot Individual

Answer: C

Explanation:
GitHub Copilot Enterprise is the most cost-effective plan for managing users with an Identity Provider like Okta, as it provides enterprise-level features and integration.

NEW QUESTION # 16
Which of the following describes role prompting?

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

Answer: C

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 # 17
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