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| Topic | Details |
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| Topic 1 | - 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 2 | - Domain 6: Testing with GitHub Copilot: This section measures abilities of QA Engineers and Test Automation Specialists to use Copilot for test generation, including unit and integration tests. It explains how Copilot can identify edge cases and assist in writing assertions. The domain also covers different Copilot subscription SKUs, privacy considerations, organizational code suggestion settings, and configuration files related to Copilot.
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| Topic 3 | - How GitHub Copilot Works and Handles Data: Designed for Machine Learning Engineers and Data Privacy Specialists, this section covers the data lifecycle and processing behind Copilot’s code suggestions. It explains how context is gathered, prompts constructed, responses generated, and post-processed through proxy services. Candidates understand Copilot’s data policies, handling of inputs, and limitations such as context window size and data age influencing suggestion relevance.
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| Topic 4 | - 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 5 | - 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 (Q128-Q133):
NEW QUESTION # 128
Which of the following statements correctly describes how GitHub Copilot Individual uses prompt data?
(Each correct answer presents part of the solution. Choose two.)
- A. Prompt data is used internally by GitHub for improving the search engine.
- B. Prompt data is used to train machine learning models for better code suggestions.
- C. Real-time user input helps generate context-aware code suggestions.
- D. Prompt data is stored unencrypted for faster processing.
Answer: B,C
Explanation:
Exact extracts:
* "For GitHub Copilot Individual, prompts and code completions may be retained and used to train machine learning models to improve Copilot." References: GitHub Copilot data usage and privacy documentation.
NEW QUESTION # 129
How can GitHub Copilot assist in maintaining consistency across your tests?
- A. By providing documentation references based on industry best practices.
- B. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.
- C. By automatically fixing all tests in the code based on the context.
- D. By writing the implementation code for the function based on context.
Answer: B
Explanation:
"Copilot learns from the patterns in your existing tests and suggests similar structures, which helps maintain consistency across the test suite." This confirms that Copilot supports test consistency through pattern recognition and suggestion.
References: GitHub Copilot testing documentation.
NEW QUESTION # 130
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)
- A. "Refactor my application to meet the latest coding standards."
- B. "Suggest ways to enhance the maintainability of this code segment."
- C. "Show me how to improve the readability of this function."
- D. "Predict future coding trends and update my codebase accordingly."
Answer: B,C
Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.
Reference: GitHub Copilot prompt engineering best practices.
NEW QUESTION # 131
You recently ended an interactive GitHub Copilot CLI session.
You need to start a new session that meets the following requirements:
* Provides a list of previously saved interactive sessions
* Resumes the most recently ended session
Which command should you run for each requirement? Each command may be used once, more than once, or not at all.

Answer:
Explanation:

Explanation:

NEW QUESTION # 132
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?
- A. Transparency
- B. Accountability
- C. Inclusiveness
- D. Fairness
Answer: A
Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrives at its decisions and suggestions.
Reference: Microsoft's AI principles and ethical guidelines.
NEW QUESTION # 133
......
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