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| Topic | Details |
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| Topic 1 | - Responsible :This section of the exam measures skills of AI Ethics Officers and Risk Managers and covers the responsible and ethical usage of AI technologies. It explains the risks and limitations associated with generative AI tools, including biases in training data and the need to validate AI outputs. Candidates learn how to operate AI responsibly by identifying potential harms such as bias, fairness, privacy concerns, and mitigating these harms by applying ethical AI principles.
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| Topic 2 | - Privacy Fundamentals and Context Exclusions: This domain focuses on Security Engineers and Compliance Officers and addresses improving code quality with Copilot’s test suggestions and security optimizations. It covers identification of security vulnerabilities, performance enhancements, and privacy features like content exclusions at repository and organization levels with explanation of their limitations. Candidates learn about safeguarding mechanisms such as duplication detection, contractual protections, security checks, and troubleshooting guide for common Copilot issues including context exclusions and suggestion gaps.
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| Topic 3 | - GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
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| Topic 4 | - Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.
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Microsoft GitHub Copilot Sample Questions (Q41-Q46):
NEW QUESTION # 41
How can GitHub Copilot assist developers during the requirements analysis phase of the Software Development Life Cycle (SDLC)?
- A. By managing stakeholder communication and meetings.
- B. By identifying and fixing potential requirement conflicts when using /help.
- C. By providing templates and code snippets that help in documenting requirements.
- D. By automatically generating detailed requirements documents.
Answer: C
Explanation:
GitHub Copilot can assist during the requirements analysis phase by providing templates and code snippets that aid in documenting requirements. This helps streamline the process of capturing and organizing project requirements.
Reference: GitHub Copilot documentation on SDLC assistance.
NEW QUESTION # 42
When crafting prompts for GitHub Copilot, what is a recommended strategy to enhance the relevance of the generated code?
- A. Avoid mentioning the programming language to allow for more flexible suggestions.
- B. Provide examples of expected input and output within the prompt.
- C. Write the prompt in natural language without any programming language.
- D. Keep the prompt as short as possible, using single words or brief phrases.
Answer: B
Explanation:
"To get the best results from GitHub Copilot, provide clear prompts and, when possible, include examples of expected input and output." This establishes that including examples is a recommended prompt engineering strategy.
References: GitHub Copilot prompt engineering documentation.
NEW QUESTION # 43
What role does chat history play in GitHub Copilot's code suggestions?
- A. Chat history is stored and shared with other users to enhance collaboration.
- B. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.
- C. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.
- D. Chat history is used to train the GitHub Copilot model in real-time.
Answer: C
Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.
NEW QUESTION # 44
What is a key consideration when relying on GitHub Copilot Chat's explanations of code functionality and proposed improvements?
- A. Reviewing and validating the generated output for accuracy and completeness.
- B. The explanations are primarily derived from user-provided documentation.
- C. The explanations are dynamically updated based on user feedback.
- D. GitHub Copilot Chat uses a static database for generating explanations.
Answer: A
Explanation:
While GitHub Copilot Chat can provide helpful explanations and suggestions, it's crucial to review and validate the generated output. Copilot's suggestions are based on its training data, and they may not always be perfectly accurate or complete. Human judgment is essential to ensure the quality and correctness of the code.
Reference: GitHub Copilot best practices and usage guidelines.
NEW QUESTION # 45
You start GitHub Copilot CLI from a directory named /repo/app. You store test files in a directory named
/repo/tests.
You need to add /repo/tests as a trusted directory without restarting GitHub Copilot CLI. The solution must ensure that GitHub Copilot can access and modify the files.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:


NEW QUESTION # 46
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