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GitHub GitHub-Copilot Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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	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 6	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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The GitHub CopilotCertification Exam (GitHub-Copilot) certification exam is one of the hottest and most industrial-recognized credentials that has been inspiring beginners and experienced professionals since its beginning. With the GitHub CopilotCertification Exam (GitHub-Copilot) certification exam successful candidates can gain a range of benefits which include career advancement, higher earning potential, industrial recognition of skills and job security, and more career personal and professional growth.

GitHub CopilotCertification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

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

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 # 21
How is GitHub Copilot Individual billed? (Each correct answer presents part of the solution. Choose two.)

A. Annually as a subscription
B. Monthly as a subscription
C. Free (not billed) for all open source projects
D. Monthly, as a metered service based on actual consumption

Answer: A,B

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

NEW QUESTION # 22
Which of the following is a risk associated with using AI?

A. AI algorithms are incapable of perpetuating existing biases.
B. AI eliminates the need for data privacy regulations.
C. AI replaces the need for developer opportunities in most fields.
D. AI systems can sometimes make decisions that are difficult to interpret.

Answer: D

Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.

NEW QUESTION # 23
When can GitHub Copilot still use content that was excluded using content exclusion?

A. If the content exclusion was configured at the enterprise level, and is overwritten at the organization level.
B. If the contents of an excluded file are referenced in code that is not excluded, for example function calls.
C. When the user prompts with @workspace.
D. When the repository level settings allow overrides by the user.

Answer: B

Explanation:
GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.

NEW QUESTION # 24
How can the concept of fairness be integrated into the process of operating an AI tool?

A. Focusing on accessibility will ensure fairness.
B. Training AI data and algorithms to be free from biases will ensure fairness.
C. Regularly monitoring the AI tool's performance will ensure fairness in its outputs.
D. Focusing on collecting large datasets for training will ensure fairness.

Answer: B

Explanation:
Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.

NEW QUESTION # 25
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