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Pass Guaranteed Quiz 2025 GitHub GitHub-Copilot: Trustable GitHub CopilotCertification Exam Customizable Exam Mode
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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
- 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 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
- 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 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.
GitHub CopilotCertification Exam Sample Questions (Q12-Q17):
NEW QUESTION # 12
What method can a developer use to generate sample data with GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)
- A. Utilizing GitHub Copilot's ability to create fictitious information from patterns in training data.
- B. Utilize GitHub Copilot's capability to directly access and use databases to create sample data.
- C. Leveraging GitHub Copilot's suggestions to create data based on API documentation in the repository.
- D. Leveraging GitHub Copilot's ability to independently initiate and manage data storage services.
Answer: A,C
Explanation:
GitHub Copilot can generate sample data by creating fictitious information based on patterns in its training data and by using suggestions based on API documentation within the repository.
NEW QUESTION # 13
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
- A. Accepted chat suggestions
- B. Changes to content exclusion settings
- C. Code suggestions made by GitHub Copilot
- D. Suggestions blocked by duplication detection filtering
Answer: B
NEW QUESTION # 14
What are the potential risks associated with relying heavily on code generated from GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)
- A. GitHub Copilot's suggestions may not always reflect best practices or the latest coding standards.
- B. GitHub Copilot may increase development lead time by providing irrelevant suggestions.
- C. GitHub Copilot may introduce security vulnerabilities by suggesting code with known exploits.
- D. GitHub Copilot may decrease developer velocity by requiring too much time in prompt engineering.
Answer: A,C
Explanation:
Heavy reliance on GitHub Copilot can introduce security vulnerabilities if the generated code contains known exploits. Additionally, Copilot's suggestions may not always align with best practices or the latest standards, requiring careful review and validation.
NEW QUESTION # 15
An independent contractor develops applications for a variety of different customers. Assuming no concerns from their customers, which GitHub Copilot plan is best suited?
- A. GitHub Copilot Business
- B. GitHub Copilot Individual
- C. GitHub Copilot Enterprise
- D. GitHub Copilot Teams
- E. GitHub Copilot Business for non-GHE Customers
Answer: B
Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.
NEW QUESTION # 16
What is a benefit of using custom models in GitHub Copilot?
- A. Responses use the organization's LLM engine
- B. Responses are faster to produce and appear sooner
- C. Responses use practices and patterns in your repositories
- D. Responses are guaranteed to be correct
Answer: C
Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and practices within your repositories. This results in suggestions that are more aligned with your organization's coding standards and conventions, improving the relevance and accuracy of the generated code.
NEW QUESTION # 17
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