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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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 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	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.

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GitHub CopilotCertification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
How do you generate code suggestions with GitHub Copilot in the CLI?

A. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.
B. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
C. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
D. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.

Answer: B

Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.

NEW QUESTION # 90
Which of the following are valid and commonly used commands while using GitHub Copilot in the CLI?

A. Copilot.run, Copilot.info, Copilot.chat
B. gh codegen, gh audit, gh summarize
C. gh Copilot explain, gh Copilot suggest, gh Copilot extension listright
D. gh Copilot status, gh Copilot init, git Copilot deploy

Answer: C

Explanation:
gh Copilot explain: Used to get the explanation of code or commands.
gh Copilot suggest: Ask Copilot to provide suggestions for performing a Task.
gh Copilot extension list: Displays all the available extensions and also indicates the proper installation of Copilot.
This set of commands can enable developers to interact with AI from the terminal in a seamless way.
Reference:
https://docs.github.com/en/Copilot/using-github-Copilot/using-github-Copilot-in-the-command-line

NEW QUESTION # 91
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

A. Provide links to supporting documentation
B. Provide all information about the utilized files
C. Provide specific success criteria
D. Provide insight on where to get the content from to get a response

Answer: C,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 # 92
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

A. GitHub Copilot will automatically correct any statistical errors found in the user's initial code.
B. GitHub Copilot can independently verify the statistical significance of results.
C. GitHub Copilot can design new statistical methods that have not been previously documented.
D. GitHub Copilot's suggestions are based on statistical trends and may not always apply accurately to specific datasets.

Answer: D

Explanation:
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.

NEW QUESTION # 93
What two options navigate to configure duplicate detection? Each correct answer presents part of the solution. (Choose two.)

A. Enterprise settings → Copilot → Policies
B. Organization settings → Copilot → Policies
C. User settings → Copilot → Policies
D. Repository settings → Copilot → Policies

Answer: A,B

Explanation:
Organization‑ and enterprise‑level Copilot policies (including duplicate‑detection settings) live under Copilot → Policies in both the GitHub Organization Settings and the Enterprise Settings.

NEW QUESTION # 94
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