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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 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.
Topic 5
  • 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 6
  • 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.

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GitHub CopilotCertification Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data? Each correct answer presents part of the solution. (Choose two.)

Answer: A,D

Explanation:
Copilot uses your real‑time input (the prompt and surrounding code) to generate relevant, context
‑aware suggestions on the fly.
Under the Individual plan, unless you opt out, your prompts and accepted suggestions are fed back into GitHub's model‑training pipeline to improve future code completions.


NEW QUESTION # 36
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

Answer: D

Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.


NEW QUESTION # 37
While working in Visual Studio Code, a developer notices inline code suggestions appearing as they type. The suggestions seem to adapt based on comments and previously written code. What does this behavior best describe about GitHub Copilot in the IDE?

Answer: B

Explanation:
GitHub Copilot in IDEs like VS Code works by analyzing the surrounding code and developer comments in real-time. It uses this context to suggest relevant completions, often inline as you type.
Reference:
https://learn.microsoft.com/en-us/training/modules/introduction-to-github-Copilot/3-interacting-with-Copilot


NEW QUESTION # 38
Which of the following steps correctly demonstrates how to establish an organization-wide policy for GitHub Copilot Business to restrict its use to certain repositories?

Answer: A

Explanation:
Organization-wide Copilot Business policies are set up in your GitHub organization's settings, where you can specify exactly which repositories are allowed or blocked for Copilot access.


NEW QUESTION # 39
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
GitHub Copilot Individual is billed as a monthly or annual subscription.


NEW QUESTION # 40
......

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