Microsoft GH-300 Online Practice Questions and Exam Preparation

Microsoft GH-300 Online Questions & Answers

• Question 91:

  A social media manager wants to use AI to filter content. How can they promote transparency in the platform' s AI operations?

  A. By regularly updating the AI filtering algorithm.
  B. By relying on a well-regarded AI development company.
  C. By focusing on user satisfaction with the content filtering.
  D. By providing clear s about the types of content the AI is designed to filter and how it arrives at its conclusion.
  D. By providing clear s about the types of content the AI is designed to filter and how it arrives at its conclusion.

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  Explanation

  "Transparency. AI systems should be understandable."

  References: Microsoft Responsible AI guidelines and transparency notes.

• Question 92:

  What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

  A. Provide specific success criteria
  B. Provide all information about the utilized files
  C. Provide insight on where to get the content from to get a response
  D. Provide links to supporting documentation
  A. Provide specific success criteria
  D. Provide links to supporting documentation

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  Explanation

  Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.

  GitHub Copilot prompt engineering best practices.

• Question 93:

  Which GitHub Copilot pricing plans include features that exclude your GitHub Copilot data like usage, prompts, and suggestions from default training GitHub Copilot? (Choose two correct answers.)

  A. GitHub Copilot Business
  B. GitHub Copilot Codespace
  C. GitHub Copilot Individual
  D. GitHub Copilot Enterprise
  A. GitHub Copilot Business
  D. GitHub Copilot Enterprise

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  Explanation

  "For Copilot Business and Copilot Enterprise, user data such as code suggestions, prompts, and completions are excluded from training GitHub Copilot's models."

  This confirms that only Business and Enterprise plans provide this exclusion feature.

  References: GitHub Copilot data usage and privacy documentation.

• Question 94:

  How can GitHub Copilot assist in maintaining consistency across your tests?

  A. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.
  B. By automatically fixing all tests in the code based on the context.
  C. By providing documentation references based on industry best practices.
  D. By writing the implementation code for the function based on context.
  A. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.

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  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.

• Question 95:

  In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?

  A. GitHub Copilot can directly convert legacy applications into cloud-native architectures. GitHub
  B. Copilot can suggest modern programming patterns based on your code. GitHub Copilot can
  C. create and deploy full-stack applications based on a single query. GitHub Copilot can refactor
  D. applications to align with upcoming standards.
  B. Copilot can suggest modern programming patterns based on your code. GitHub Copilot can

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  Explanation

  GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot

  excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs,

  propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.

  Why not A?GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn't autonomously perform the full

  conversion process, which involves architectural decisions and deployment steps beyond its scope.

  Why not C?While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve

  a complete, deployable solution.

  Why not D?GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn't inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the

  data it was trained on, not future standards that are yet to be defined.

  Thus,Bis the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.

  GitHub Copilot documentation on application modernization.

• Question 96:

  How does GitHub Copilot Chat help to fix security issues in your codebase?

  A. By enforcing strict coding standards that prevent the introduction of vulnerabilities.
  B. By providing detailed reports on the security vulnerabilities present in the codebase.
  C. By annotating the given suggestions with known vulnerability patterns.
  D. By automatically refactoring the entire codebase to remove vulnerabilities.
  C. By annotating the given suggestions with known vulnerability patterns.

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  Explanation

  "When Copilot Chat generates suggestions, it may annotate them with warnings about known insecure patterns, helping developers identify potential vulnerabilities in context."

  This indicates that Copilot Chat assists with security by annotating code suggestions with vulnerability patterns.

  References: GitHub Copilot Chat security and responsible use documentation.

• Question 97:

  How does GitHub Copilot typically handle code suggestions that involve deprecated features or syntax of programming languages?

  A. GitHub Copilot automatically updates deprecated features in its suggestions to the latest version.
  B. GitHub Copilot may suggest deprecated syntax or features if they are present in its training data.
  C. GitHub Copilot always filters out deprecated elements to promote the use of current standards.
  D. GitHub Copilot rejects all prompts involving deprecated features to avoid compilation errors.
  B. GitHub Copilot may suggest deprecated syntax or features if they are present in its training data.

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  Explanation

  "GitHub Copilot may sometimes suggest deprecated code, APIs, or patterns if these appear in its training data. Users are responsible for reviewing and updating the suggestions."

  This confirms that Copilot does not automatically update or reject deprecated features, but may still suggest them if they were part of training.

  References: GitHub Copilot usage limitations documentation.

• Question 98:

  How can the concept of fairness be integrated into the process of operating an AI tool?

  A. Focusing on accessibility will ensure fairness.
  B. Focusing on collecting large datasets for training will ensure fairness.
  C. Regularly monitoring the AI tool's performance will ensure fairness in its outputs.
  D. Training AI data and algorithms to be free from biases will ensure fairness.
  D. Training AI data and algorithms to be free from biases will ensure fairness.

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  Explanation

  Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.

  Microsoft's AI principles and fairness guidelines.

• Question 99:

  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.
  C. GitHub Copilot's reliance on pattern-based responses without verifying computation accuracy.

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  Explanation

  "Copilot's responses are generated based on patterns in training data and are not guaranteed to be mathematically accurate. Developers must verify calculations independently."

  This shows that Copilot cannot guarantee correctness in math and relies on patterns, making option C correct.

  References: GitHub Copilot responsible use documentation.

• Question 100:

  How does GitHub Copilot assist developers in reducing the amount of manual boilerplate code they write?

  A. By engaging in real-time collaboration with multiple developers to write boilerplate code.
  B. By predicting future coding requirements and pre-emptively generating boilerplate code.
  C. By refactoring the entire codebase to eliminate boilerplate code without developer input.
  D. By suggesting code snippets that can be reused across different parts of the project.
  D. By suggesting code snippets that can be reused across different parts of the project.

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  Explanation

  "Copilot helps reduce boilerplate by suggesting reusable code snippets for repetitive structures across your project."

  This proves that Copilot saves time by generating reusable snippets, not by preemptively or automatically refactoring entire codebases.

  References: GitHub Copilot productivity documentation.