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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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
What are the additional checks that need to pass before the GitHub Copilot responses are submitted to the user? Each correct answer presents part of the solution. (Choose two.)
- A. Performance benchmarking
- B. Suggestions matching public code (optional based on settings)
- C. Compatibility with user-specific settings
- D. Code quality
Answer: B,D
Explanation:
fter the model generates a suggestion, Copilot runs it through post‑processing filters that test for obvious bugs and security vulnerabilities (Code quality) and, if enabled, block any suggestion that matches public repository code over ~150 characters (Suggestions matching public code) before returning it to the user.
NEW QUESTION # 90
What is the main purpose of the duplication detection filter in GitHub Copilot?
- A. To compare user-generated code against a private repository for potential matches.
- B. To encourage the user to follow coding best practices preventing code duplication.
- C. To detect and block suggestions that match public code snippets on GitHub if they contain about
150 characters. - D. To allow administrators to control which suggestions are visible to developers based on custom criteria.
Answer: C
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 # 91
What is one of the recommended practices when using GitHub Copilot Chat to enhance code quality?
- A. Avoid using Copilot for complex tasks.
- B. Rely solely on Copilot's suggestions without reviewing them.
- C. Regularly review and refactor the code suggested by Copilot.
- D. Disable Copilot's inline suggestions.
Answer: C
Explanation:
Regularly reviewing and refactoring Copilot's output helps ensure that generated code adheres to your project's quality standards and catches any issues early.
NEW QUESTION # 92
Which of the following are true about code suggestions? Each correct answer presents part of the solution. (Choose two.)
- A. Code suggestions are guaranteed to not expose known security vulnerabilities
- B. You can use keyboard shortcuts to accept the next word in a suggestion
- C. Code suggestions are limited to single-line suggestions
- D. Alternative code suggestions can be shown in a new tab
- E. Code suggestions will always compile or run without modifications
Answer: B,D
Explanation:
You can use keyboard shortcuts (e.g., Tab or Ctrl+→) to accept the next word (or token) of an inline suggestion.
When you request alternative completions, Copilot opens them in the Copilot pane (a new editor tab) where you can browse and insert the option you prefer.
NEW QUESTION # 93
In which of the following scenarios is GitHub Copilot Chat most effectively utilized?
- A. Generating pull request summaries
- B. Configuring organization-wide access controls
- C. Asking questions about project-specific code behaviorright
- D. Viewing repository audit logs
Answer: C
Explanation:
GitHub Copilot Chat excels at providing context-aware assistance directly within the developer's IDE. When a developer asks a question about a function, class, or logic in their codebase, Copilot Chat can interpret the context and offer accurate, helpful responses, such as explanations of what a piece of code does, how to refactor it, or how to fix an error. This makes it particularly effective for in-the-moment learning and debugging during software development.
Reference:
https://docs.github.com/en/Copilot/about-github-Copilot/what-is-github-Copilot#Copilot-features
NEW QUESTION # 94
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