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Hohe Qualität von GitHub-Copilot Prüfung und Antworten
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GitHub GitHub-Copilot Prüfungsplan:
Thema
Einzelheiten
Thema 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.
Thema 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.
Thema 3
- 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.
Thema 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.
Thema 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.
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GitHub CopilotCertification Exam GitHub-Copilot Prüfungsfragen mit Lösungen (Q15-Q20):
15. Frage
Which of the following scenarios best describes the intended use of GitHub Copilot Chat as a tool?
- A. A solution for software development, requiring no additional input or oversight.
- B. A tool solely designed for debugging and error correction.
- C. A complete replacement for developers generating code.
- D. A productivity tool that provides suggestions, but relying on human judgment.
Antwort: D
Begründung:
GitHub Copilot Chat is designed to be a productivity enhancer, not a replacement for human developers. It provides suggestions and assists with coding tasks, but the final decision and validation always rest with the developer. Copilot Chat is meant to augment the developer's workflow, making it faster and more efficient, but it does not remove the need for human oversight and judgment.
16. Frage
What GitHub Copilot feature can be configured at the organization level to prevent GitHub Copilot suggesting publicly available code snippets?
- A. GitHub Copilot duplication detection filter
- B. GitHub Copilot Chat in the IDE
- C. GitHub Copilot Chat in GitHub Mobile
- D. GitHub Copilot access to Bing
Antwort: A
Begründung:
The duplication detection filter can be configured at the organization level to prevent GitHub Copilot from suggesting publicly available code snippets.
17. Frage
Which of the following statements best describes the impact of GitHub Copilot on the software development process?
- A. It reduces overhead by automating testing workflows.
- B. It increases productivity by automating repetitive coding tasks.
- C. It decreases software vulnerabilities from third party dependencies.
- D. It replaces the need for developers in the software development process.
Antwort: B
Begründung:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
18. Frage
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 may decrease developer velocity by requiring too much time in prompt engineering.
- 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's suggestions may not always reflect best practices or the latest coding standards.
Antwort: C,D
Begründung:
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.
19. Frage
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)
- A. Creation of a unit test scenario for newly developed Python code
- B. Create a technical requirement specification from the business requirement documentation
- C. Explain a legacy COBOL code and translate the code to another language like Python.
- D. Creation of end-to-end performance testing scenarios for a web application
Antwort: A,C
Begründung:
GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.
20. Frage
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