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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 | - 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.
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| 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.
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| Topic 4 | - 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 5 | - 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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| Topic 6 | - 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.
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GitHub CopilotCertification Exam Sample Questions (Q119-Q124):
NEW QUESTION # 119
What are the potential limitations of GitHub Copilot in maintaining existing codebases?
- A. GitHub Copilot's suggestions are always aware of the entire codebase.
- B. GitHub Copilot might not fully understand the context and dependencies within a large codebase.
- C. GitHub Copilot can refactor and optimize the entire codebase up to 10,000 lines of code.
- D. GitHub Copilot can independently manage and resolve all merge conflicts in version control.
Answer: B
Explanation:
GitHub Copilot operates within a limited context window and may not capture all dependencies or the full project structure in a large codebase, so it can misunderstand context and require developer oversight.
NEW QUESTION # 120
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
- A. Changes to content exclusion settings
- B. Code suggestions made by GitHub Copilot
- C. Accepted chat suggestions
- D. Suggestions blocked by duplication detection filtering
Answer: A
NEW QUESTION # 121
When using GitHub Copilot Chat to generate unit tests, which slash command would you use?
- A. /generate-tests
- B. /init-tests
- C. /tests
- D. /create-tests
Answer: A
Explanation:
The /generate-tests slash command instructs Copilot Chat to create unit tests for the selected code.
NEW QUESTION # 122
What is used by GitHub Copilot in the IDE to determine the prompt context?
- A. Information from the IDE like open tabs, cursor location, selected code.
- B. All the code in the current repository and any git submodules.
- C. All the code visible in the current IDE.
- D. The open tabs in the IDE and the current folder of the terminal.
Answer: A
Explanation:
Copilot gathers context directly from your IDE - including which files or tabs are open, where your cursor is positioned, and any selected code - to tailor its suggestions to exactly where and how you're coding.
NEW QUESTION # 123
What types of prompts or code snippets might be flagged by the GitHub Copilot toxicity filter?
(Each correct answer presents part of the solution. Choose two.)
- A. Hate speech or discriminatory language (e.g., racial slurs, offensive stereotypes)
- B. Code comments containing strong opinions or criticisms
- C. Code that contains logical errors or produces unexpected results
- D. Sexually suggestive or explicit content
Answer: A,D
Explanation:
GitHub Copilot includes a toxicity filter to prevent the generation of harmful or inappropriate content. This filter flags prompts or code snippets that contain hate speech, discriminatory language, or sexually suggestive or explicit content. This ensures a safe and respectful coding environment.
NEW QUESTION # 124
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