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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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
  • 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 5
  • 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 6
  • 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 CopilotCertification Exam Sample Questions (Q98-Q103):

NEW QUESTION # 98
How does GitHub Copilot Enterprise assist in code reviews during the pull request process?
(Select two.)

Answer: C,D

Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.


NEW QUESTION # 99
What is the impact of the "Fill-In-the-Middle" (FIM) technique on GitHub Copilot's code suggestions?

Answer: B

Explanation:
The Fill-In-the-Middle technique lets Copilot look at both what comes before and after your cursor, so it can generate the "missing" code between those segments more accurately.


NEW QUESTION # 100
What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution.
Choose two.)

Answer: A,C

Explanation:
GitHub Copilot Chat has limitations such as limited training data, which can affect the accuracy of its suggestions, and it does not provide extensive support for all programming languages.


NEW QUESTION # 101
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)

Answer: B,D

Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.


NEW QUESTION # 102
Which Copilot Enterprise features are available in all commercially supported IDEs?

Answer: C,D

Explanation:
Chat: The Copilot Chat pane is integrated into every commercially supported IDE, letting you ask coding questions and get conversational assistance directly in your editor.
Inline suggestions: Real‑time code completions ("inline suggestions") appear as you type in all supported IDEs, providing context‑aware code snippets without leaving your editor.


NEW QUESTION # 103
......

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