Microsoft GH-300 Online Practice Questions and Exam Preparation

Microsoft GH-300 Online Questions & Answers

• Question 111:

  Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

  A. Accepted chat suggestions
  B. Code suggestions made by GitHub Copilot
  C. Changes to content exclusion settings
  D. Suggestions blocked by duplication detection filtering
  C. Changes to content exclusion settings

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  Explanation

  Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.

  GitHub Copilot Business audit logs.

• Question 112:

  When using GitHub Copilot to identify missing tests in your codebase, which of the following is the most important factor to consider?

  A. Using well-known coding practices in your repository.
  B. Ensuring that the correct context is available to GitHub Copilot.
  C. Close all the tabs in your IDE that do not have tests in them.
  D. Having a high test coverage percentage in the codebase.
  B. Ensuring that the correct context is available to GitHub Copilot.

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  Explanation

  "The accuracy of Copilot's test generation depends heavily on the available context, including existing code, file structure, and open selections."

  This confirms that providing the correct context is the most important factor.

  References: GitHub Copilot testing documentation.

• Question 113:

  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
  A. Limited training data
  C. Ability to handle complex code structures

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  Explanation

  "GitHub Copilot Chat, like all AI models, is limited by its training data and may not always provide accurate or up-to-date answers."

  References: GitHub Copilot Chat limitations documentation.

• Question 114:

  How can the insights gained from the metrics API be used to improve the development process in conjunction with GitHub Copilot?

  A. Real-time debugging and error resolution statistics.
  B. Insights on the types of coding languages where GitHub Copilot is most helpful.
  C. Automated generation of complete project documentation.
  D. Detailed analysis of GitHub Copilot's suggestions vs. manual coding.
  D. Detailed analysis of GitHub Copilot's suggestions vs. manual coding.

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  Explanation

  "The Copilot metrics API provides insights into how suggestions are being accepted and used, enabling teams to analyze the relative impact of Copilot-generated code versus manually written code."

  This confirms option D is correct, as the API enables analysis of Copilot versus manual coding.

  References: GitHub Copilot metrics API documentation.

• Question 115:

  How can GitHub Copilot assist developers during the requirements analysis phase of the Software Development Life Cycle (SDLC)?

  A. By automatically generating detailed requirements documents.
  B. By providing templates and code snippets that help in documenting requirements.
  C. By identifying and fixing potential requirement conflicts when using /help.
  D. By managing stakeholder communication and meetings.
  B. By providing templates and code snippets that help in documenting requirements.

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  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.

  GitHub Copilot documentation on SDLC assistance.

• Question 116:

  How does GitHub Copilot identify matching code and ensure that public code is appropriately handled or blocked? (Each correct answer presents part of the solution. Choose two.)

  A. Using machine learning models trained only on private repositories
  B. Reviewing and storing user-specific private repository data for future suggestions
  C. Filtering out suggestions that match code from public repositories
  D. Implementing safeguards to detect and avoid suggesting verbatim snippets from public code
  C. Filtering out suggestions that match code from public repositories
  D. Implementing safeguards to detect and avoid suggesting verbatim snippets from public code

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  Explanation

  "GitHub Copilot has a filter to detect code suggestions that match public code on GitHub. When this filter is enabled, Copilot checks suggestions with about 150 characters or more against public code and blocks them if a match is found."

  References: GitHub Copilot duplication detection and safeguards documentation.

• Question 117:

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

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

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  Explanation

  GitHub Copilot can still use excluded content if it is referenced in code that is not excluded, such as function calls.

  GitHub Copilot content exclusion documentation.

• Question 118:

  Which of the following are true about code suggestions? (Each correct answer presents part of the solution. Choose two.)

  A. Code suggestions are guaranteed to not expose known security vulnerabilities
  B. You can use keyboard shortcuts to accept the next word in a suggestion
  C. Code suggestions are limited to single-line suggestions
  D. Code suggestions will always compile or run without modifications
  E. Alternative code suggestions can be shown in a new tab
  B. You can use keyboard shortcuts to accept the next word in a suggestion
  E. Alternative code suggestions can be shown in a new tab

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  Explanation

  "You can accept individual words, lines, or entire suggestions with keyboard shortcuts."

  References: GitHub Copilot editor usage documentation.

• Question 119:

  Are there any limitations to consider when using GitHub Copilot for code refactoring?

  A. GitHub Copilot may not always produce optimized or best-practice code for refactoring.
  B. GitHub Copilot can only be used with a limited set of programming languages.
  C. GitHub Copilot always produces bug-free code during refactoring.
  D. GitHub Copilot understands the context of your entire project and refactors code accordingly.
  A. GitHub Copilot may not always produce optimized or best-practice code for refactoring.

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  Explanation

  "While Copilot can suggest refactoring changes, the code may not always follow best practices or be fully optimized. Developers must review and validate suggestions."

  This confirms that limitations exist in optimization and best practices, making option A correct.

  References: GitHub Copilot limitations documentation.

• Question 120:

  How long does GitHub retain Copilot data for Business and Enterprise? (Each correct answer presents part of the solution. Choose two.)

  A. Prompts and Suggestions: Not retained
  B. Prompts and Suggestions: Retained for 28 days
  C. User Engagement Data: Kept for Two Years
  D. User Engagement Data: Kept for One Year
  B. Prompts and Suggestions: Retained for 28 days
  C. User Engagement Data: Kept for Two Years

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  Explanation

  For GitHub Copilot Business and Enterprise, prompts and suggestions are retained for 28 days to provide context and improve the service. User engagement data, which includes usage patterns and interactions, is kept for two years. This data retention policy is designed to balance service improvement with user privacy.

  GitHub Copilot documentation on data privacy and retention policies for Business andEnterprise plans.