[Oct-2025] Microsoft GH-300 Actual Questions and Braindumps [Q14-Q29]

[Oct-2025] Microsoft GH-300 Actual Questions and Braindumps

Pass GH-300 Exam with Updated GH-300 Exam Dumps PDF 2025

Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	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.
Topic 4	Domain 4: Prompt Crafting and Prompt Engineering This section measures skills of Software Developers and AI Interaction Designers in effectively crafting prompts to optimize Copilot’s output. It reviews foundational concepts such as prompt components, the role of language in prompting, zero-shot vs. few-shot prompting, and how chat history influences responses. Best practices and engineering principles for prompt design and training methods are also covered.

 

NEW QUESTION # 14
Which GitHub Copilot plan allows for prompt and suggestion collection?

• A. GitHub Copilot Individuals
• B. GitHub Copilot Enterprise
• C. GitHub Copilot Business
• D. GitHub Copilot Codespace

Answer: B

Explanation:
GitHub Copilot Enterprise allows for prompt and suggestion collection, enabling organizations to analyze and improve their usage of the tool.

NEW QUESTION # 15
How does GitHub Copilot Chat utilize its training data and external sources to generate responses when answering coding questions?

• A. It uses user-provided documentation exclusively to generate responses.
• B. It primarily uses search results from Bing to generate responses.
• C. It combines its training data set, code in user repositories, and external sources like Bing to generate responses.
• D. It primarily relies on the model's training data to generate responses.

Answer: C

Explanation:
GitHub Copilot Chat combines its training data, code from user repositories, and external sources like Bing to generate comprehensive and relevant responses to coding questions.

NEW QUESTION # 16
Which Microsoft ethical AI principle is aimed at ensuring AI systems treat all people equally?

• A. Reliability and Safety
• B. Inclusiveness
• C. Fairness
• D. Privacy and Security

Answer: C

Explanation:
The principle of fairness ensures that AI systems treat all people equally and avoid discriminatory outcomes.

NEW QUESTION # 17
How does GitHub Copilot Chat ensure that a function works correctly?

• A. By suggesting assertions based on the code's context and semantics.
• B. By writing the implementation code for the function.
• C. By executing the test cases to validate the correctness of the code.
• D. By automatically writing all the tests for the function.

Answer: A

Explanation:
GitHub Copilot Chat can suggest assertions based on the code's context and semantics to help developers verify the correctness of their functions. These assertions serve as checks that the function behaves as expected under various conditions.

NEW QUESTION # 18
When can GitHub Copilot still use content that was excluded using content exclusion?

• A. When the repository level settings allow overrides by the user.
• B. If the contents of an excluded file are referenced in code that is not excluded, for example function calls.
• 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.

Answer: B

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

NEW QUESTION # 19
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?

• A. Enforce blocking of MIT or GPL licensed code
• B. Enable GitHub Copilot license checking
• C. Suggestions matching public code to 'blocked'
• D. You need to check code suggestions yourself before accepting

Answer: C

Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.

NEW QUESTION # 20
Select a strategy to increase the performance of GitHub Copilot Chat.

• A. Apply prompt engineering techniques to be more specific
• B. Limit the number of concurrent users accessing GitHub Copilot Chat
• C. Optimize the usage of memory-intensive operations within generated code
• D. Use a single GitHub Copilot Chat query to find resolutions for the collection of technical requirements

Answer: A

Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.

NEW QUESTION # 21
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?

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

Answer: B

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

NEW QUESTION # 22
Which Copilot Individual features are available when using a supported extension for Visual Studio, VS Code, or JetBrains IDEs? (Each correct answer presents part of the solution. Choose two.)

• A. Pull Request Diff Analysis
• B. Code suggestions
• C. Chat
• D. Knowledge Base

Answer: B,C

Explanation:
GitHub Copilot Individual provides code suggestions and chat features when used with supported IDE extensions like Visual Studio, VS Code, and JetBrains IDEs.

NEW QUESTION # 23
How does GitHub Copilot Enterprise assist in code reviews during the pull request process? (Select two.)

• A. It can validate the accuracy of the changes in the pull request.
• B. It automatically merges pull requests after an automated review.
• C. It can answer questions about the changeset of the pull request.
• D. It generates a prose summary and a bulleted list of key changes for pull requests.

Answer: C,D

Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.

NEW QUESTION # 24
What is a limitation of content exclusions?

• A. Content exclusions can only be configured by an enterprise administrator.
• B. Repository administrators and organization owners cannot manage content exclusion settings.
• C. Content exclusions are only available in the GitHub Copilot Individual plan.
• D. Content exclusions can be worked around as it is only available for Git repositories.

Answer: D

Explanation:
A limitation is that content exclusions are only available for Git repositories, meaning they can be worked around if content is accessed through other means.

NEW QUESTION # 25
How do you generate code suggestions with GitHub Copilot in the CLI?

• A. Write code comments -> Press the suggestion shortcut -> Select the best suggestion from the list.
• B. Describe the project's architecture -> Use the copilot generate command -> Accept the generated suggestion.
• C. Type out the code snippet -> Use the copilot refine command to enhance it -> Review the suggested command.
• D. Use copilot suggest -> Write the command you want -> Select the best suggestion from the list.

Answer: A

Explanation:
In the CLI, GitHub Copilot generates code suggestions by analyzing code comments. You write comments describing what you want, and Copilot provides relevant code suggestions. You then select the best suggestion from the list.

NEW QUESTION # 26
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 automatically generating detailed requirements documents.
• D. By providing templates and code snippets that help in documenting requirements.

Answer: D

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.

NEW QUESTION # 27
How can GitHub Copilot assist with code refactoring tasks?

• A. GitHub Copilot can suggest refactoring improvements for better code quality.
• B. GitHub Copilot can remove unnecessary files from the project directory.
• C. GitHub Copilot can fix syntax errors without user input.
• D. GitHub Copilot can automatically rewrite code to follow best practices.

Answer: A

Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.

NEW QUESTION # 28
Why might a Generative AI (Gen AI) tool create inaccurate outputs?

• A. The Gen AI tool is experiencing downtime and is not fully recovered.
• B. The training data might contain biases or inconsistencies.
• C. The Gen AI tool is programmed with a focus on creativity over factual accuracy.
• D. The Gen AI tool is overloaded with too many requests at once.

Answer: B

Explanation:
Gen AI tools can produce inaccurate outputs if the training data contains biases or inconsistencies, which can lead to flawed or misleading results.

NEW QUESTION # 29
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