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GitHub GitHub-Copilot Exam Overview:
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| Topic | Details |
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| Topic 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.
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| 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.
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| Topic 3 | - 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 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.
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GitHub CopilotCertification Exam Sample Questions (Q107-Q112):
NEW QUESTION # 107
How does GitHub Copilot Enterprise assist in code reviews during the pull request process?
(Select two.)
- A. It generates a prose summary and a bulleted list of key changes for pull requests.
- B. It can answer questions about the changeset of the pull request.
- C. It can validate the accuracy of the changes in the pull request.
- D. It automatically merges pull requests after an automated review.
Answer: A,B
Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.
NEW QUESTION # 108
How does GitHub Copilot Chat help in understanding the existing codebase?
- A. By providing visual diagrams of the code structure.
- B. By answering questions about the code and generating explanations.
- C. By automatically refactoring code to improve readability.
- D. By running code linters and formatters.
Answer: B
Explanation:
GitHub Copilot Chat helps in understanding existing codebases by answering questions about the code and generating explanations. This allows developers to quickly grasp the functionality and structure of unfamiliar code.
NEW QUESTION # 109
As a developer who is evaluating the efficiency of GitHub Copilot in generating accurate code across different problem domains. During your R&D, you want to consider the limitations of GenAI tools like Copilot. Which of the following options best describes a core limitation?
- A. Copilot may return output that reflects bias or inaccurate info because of its training dataright
- B. Copilot always generates error-free code across all languages
- C. Copilot reduces the need for STLC and code reviews
- D. Copilot can only be used offline without internet access
Answer: A
Explanation:
Copilot is trained on a vast amount of data, which is publicly available; this data may include the biased, outdated, or incorrect practices of code implementation. This typically highlights a key concern in Responsible AI.
References:
https://learn.microsoft.com/en-us/training/modules/responsible-ai-with-github-Copilot/2-manage-ai-risks
NEW QUESTION # 110
What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution.
Choose two.)
- A. No biases in code suggestions
- B. Limited training data
- C. Ability to handle complex code structures
- D. Extensive support for all programming languages
Answer: B,D
Explanation:
GitHub Copilot Chat has limitations such as limited training data, which can affect the accuracy of its suggestions, and it does not provide extensive support for all programming languages.
NEW QUESTION # 111
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 Individual
- B. GitHub Copilot Business
- C. GitHub Copilot Enterprise
- D. GitHub Copilot Business for non-GHE Customers
- E. GitHub Copilot Teams
Answer: A
Explanation:
For an independent contractor, GitHub Copilot Individual is the most suitable and cost-effective plan.
NEW QUESTION # 112
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