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GitHub GitHub-Copilot Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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 5
  • 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.

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GitHub-Copilot Actual Exam & GitHub-Copilot Study Materials & GitHub-Copilot Test Torrent

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GitHub CopilotCertification Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
How can users provide feedback about GitHub Copilot Chat using their IDE?

Answer: D

Explanation:
In supported IDEs (e.g., Visual Studio or JetBrains), you can click the "Share Feedback" (or
"Send feedback") button directly in the Copilot Chat panel to submit likes, dislikes, and comments on any response.


NEW QUESTION # 74
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

Answer: C,D

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.


NEW QUESTION # 75
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

Answer: B,D

Explanation:
To improve prompt quality for GitHub Copilot (especially Copilot Chat), good prompts should:
Clearly describe what "success" looks like
Specify where the context comes from (e.g., which file, function, or problem area) Be focused, not overloaded with unnecessary information


NEW QUESTION # 76
What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

Answer: B,C,E

Explanation:
The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.


NEW QUESTION # 77
What is the main purpose of the duplication detection filter in GitHub Copilot?

Answer: B

Explanation:
The duplication detection filter checks any suggestion against public GitHub code - examining roughly 150 characters of generated code and its surrounding context - and blocks suggestions that match or closely resemble public snippets.


NEW QUESTION # 78
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