Exam Microsoft GH-300 Questions Answers & GH-300 Accurate Prep Material

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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft / GitHub
Exam Name:	GitHub Copilot Exam (GH-300)
Exam Number:	GH-300
Passing Score:	700/1000
Exam Price:	USD 99
Related Certifications:	GitHub Copilot Fundamentals Part 2 GitHub Copilot Fundamentals Part 1
Real Exam Qty:	60-75 (approximately 65 scored + unscored items)
Certificate Validity Period:	2 years
Exam Format:	Multiple response, Scenario-based questions, Drag and drop, Multiple choice
Exam Duration:	100-120
Available Languages:	English, Spanish, Korean, Japanese, Portuguese (Brazil)
Recommended Training:	GitHub Copilot Fundamentals Part 2 GitHub Copilot Fundamentals Part 1
Exam Registration:	Pearson VUE Scheduling Microsoft Learn Certification Page
Sample Questions:	Microsoft GH-300 Sample Questions
Exam Way:	Online proctored (Pearson VUE) or test center
Pre Condition:	Basic GitHub knowledge and experience using at least one programming language; familiarity with GitHub Copilot recommended but not strictly required
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/github-copilot/

>> Exam Microsoft GH-300 Questions Answers <<

2026 Marvelous GH-300: Exam GitHub Copilot Questions Answers

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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
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.

Microsoft GitHub Copilot Sample Questions (Q83-Q88):

NEW QUESTION # 83
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. Code suggestions
B. Knowledge Base
C. Chat
D. Pull Request Diff Analysis

Answer: A,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 # 84
What are the potential limitations of GitHub Copilot Chat? (Each correct answer presents part of the solution.
Choose two.)

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

Answer: A,B

Explanation:
Exact extracts:
* "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.

NEW QUESTION # 85
What is one of the recommended practices when using GitHub Copilot Chat to enhance code quality?

A. Disable Copilot's inline suggestions.
B. Rely solely on Copilot's suggestions without reviewing them.
C. Regularly review and refactor the code suggested by Copilot.
D. Avoid using Copilot for complex tasks.

Answer: C

Explanation:
"Developers should always review, test, and refactor code generated by GitHub Copilot. Copilot can speed up development, but maintaining code quality requires careful human oversight." This establishes that regularly reviewing and refactoring Copilot suggestions is the recommended best practice.
References: GitHub Copilot responsible use guidelines.

NEW QUESTION # 86
How does the /tests slash command assist developers?

A. Integrates with external testing frameworks.
B. Executes test cases to find issues with the code.
C. Creates unit tests for the selected code.
D. Constructs detailed test documentation.

Answer: C

Explanation:
The /tests slash command in GitHub Copilot Chat creates unit tests for the selected code, helping developers ensure the functionality and reliability of their code.
Reference: GitHub Copilot Chat command documentation.

NEW QUESTION # 87
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 stored unencrypted for faster processing.
C. Prompt data is used to train machine learning models for better code suggestions.
D. Real-time user input helps generate context-aware code suggestions.

Answer: C,D

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 # 88
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