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Microsoft GH-300 Exam Dumps

Microsoft GH-300 Exam Dumps

GitHub Copilot Exam

123 Questions & Answers with Explanation
Update Date : July 16, 2026
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Microsoft GH-300 Sample Questions

Question # 1

When can GitHub Copilot still use content that was excluded using content exclusion? 

A. When the user prompts with @workspace. 
B. When the repository-level settings allow overrides by the user. 
C. If the contents of an excluded file are referenced in code that is not excluded, for example function calls. 
D. If the content exclusion was configured at the enterprise level, and is overwritten at the organization level. 



Question # 2

How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution. Choose two.) 

A. By opening the relevant tabs in your IDE 
B. By adding relevant code snippets to your prompt 
C. By adding the important files to your .gitconfig 
D. By adding the full file paths to your prompt of important files 



Question # 3

In what ways can GitHub Copilot contribute to the design phase of the Software Development Life Cycle (SDLC)? 

A. GitHub Copilot can independently create a complete software design. 
B. GitHub Copilot can suggest design patterns and best practices relevant to the project. 
C. GitHub Copilot can manage design team collaboration and version control. 
D. GitHub Copilot can generate user interface (UI) prototypes without prompting. 



Question # 4

What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution. Choose two.) 

A. Limited training data 
B. No biases in code suggestions 
C. Ability to handle complex code structures 
D. Extensive support for all programming languages  



Question # 5

What are the potential limitations of GitHub Copilot in maintaining existing codebases? 

A. GitHub Copilot can independently manage and resolve all merge conflicts in version control. 
B. GitHub Copilot might not fully understand the context and dependencies within a large codebase. 
C. GitHub Copilot's suggestions are always aware of the entire codebase. 
D. GitHub Copilot can refactor and optimize the entire codebase up to 10,000 lines of code. 



Question # 6

What caution should developers exercise when using GitHub Copilot for assistance with mathematical computations? 

A. GitHub Copilot's capability to optimize complex mathematical algorithms beyond manual coding. 
B. GitHub Copilot's ability to execute and verify mathematical results in real-time. 
C. GitHub Copilot's reliance on pattern-based responses without verifying computation accuracy. 
D. GitHub Copilot's automatic update of outdated mathematical formulas to modern standards. 



Question # 7

How can GitHub Copilot be limited when it comes to suggesting unit tests? 

A. GitHub Copilot can generate all types of unit tests, including those for edge cases and complex integration scenarios. 
B. GitHub Copilot primarily suggests basic unit tests that focus on core functionalities, often requiring additional input from developers for comprehensive coverage. 
C. GitHub Copilot can handle any complexity in code and automatically generate appropriate unit tests. 
D. GitHub Copilot's limitations in generating unit tests can vary based on the IDE version you are using. 



Question # 8

What are the different ways to give context to GitHub Copilot to get more precise responses? (Each correct answer presents part of the solution. Choose two.) 

A. Utilize to interpret developer's thoughts and intentions without any code or comments. 
B. Engage with chat participants such as @workspace to incorporate collaborative context into the responses. 
C. Access developer's previous projects and code repositories to understand their coding style without explicit permission.
 D. Utilize chat variables like *file to anchor the conversation within the specific context of the files or editors in use.



Question # 9

What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot? 

A. Running code suggestions through filters designed to detect public code 
B. Comparing suggestions against public code using machine learning. 
C. Analyzing the context and structure of the code being written 
D. Reviewing the user's browsing history to identify public repositories



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