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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 5	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.
Topic 6	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.

Reference: https://examregistration.github.com/certification/COPILOT

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GitHub CopilotCertification Sample Questions:

Question #1

Which GitHub Copilot pricing plans include features that exclude your GitHub Copilot data like usage, prompts, and suggestions from default training GitHub Copilot? (Choose two.)

A. GitHub Copilot Individual
B. GitHub Copilot Business
C. GitHub Copilot Codespace
D. GitHub Copilot Enterprise

Reveal Solution Discussion 0

Correct Answer: B,D 🗳️

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Question #2

Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data? Each correct answer presents part of the solution. (Choose two.)

A. Prompt data is stored unencrypted for faster processing.
B. Prompt data is used internally by GitHub for improving the search engine.
C. Real-time user input helps generate context-aware code suggestions.
D. Prompt data is used to train machine learning models for better code suggestions.

Reveal Solution Discussion 0

Correct Answer: C,D 🗳️

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Question #3

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 combines its training data set, code in user repositories, and external sources like Bing to generate responses.
D. It uses user-provided documentation exclusively to generate responses.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #4

How can you get multiple suggestions from GitHub Copilot?

A. By using the inline chat functionality with the command/multiple
B. By using @workspace in the chat window
C. By asking for multiple suggestions using comments in your code
D. By opening the completions panel in your editor

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #5

Which REST API endpoint is used to modify details about a GitHub Copilot Business subscription? (Each correct answer presents part of the solution. Choose two.)