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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 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.

GitHub CopilotCertification Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is a likely effect of GitHub Copilot being trained on commonly used code patterns?

A. Suggest homogeneous solutions if provided a diverse data set.
B. Suggest code snippets that reflect the most common practices in the training data.
C. Suggest innovative coding solutions that are not yet popular.
D. Suggest completely novel projects, while reducing time on a project.

Answer: B

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 # 30
What method can a developer use to generate sample data with GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. Utilizing GitHub Copilot's ability to create fictitious information from patterns in training data.
B. Leveraging GitHub Copilot's ability to independently initiate and manage data storage services.
C. Leveraging GitHub Copilot's suggestions to create data based on API documentation in the repository.
D. Utilize GitHub Copilot's capability to directly access and use databases to create sample data.

Answer: A,C

Explanation:
GitHub Copilot can generate sample data by creating fictitious information based on patterns in its training data and by using suggestions based on API documentation within the repository.

NEW QUESTION # 31
Which of the following is correct about GitHub Copilot Knowledge Bases?

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

Answer: C

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 # 32
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

A. Running code suggestions through filters designed to detect public code
B. Reviewing the user's browsing history to identify public repositories
C. Analyzing the context and structure of the code being written
D. Comparing suggestions against public code using machine learning.

Answer: A

Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.

NEW QUESTION # 33
What role does chat history play in GitHub Copilot's code suggestions?

A. Chat history is stored and shared with other users to enhance collaboration.
B. Chat history is used to train the GitHub Copilot model in real-time.
C. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.
D. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.

Answer: D

Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.

NEW QUESTION # 34
......

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Caleb Davis Caleb Davis

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