Microsoft GH-300 GitHub Copilot Questions - With 25% Discount Offer [2026]

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Microsoft GH-300 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	GitHub Copilot
Exam Number:	GH-300
Related Certifications:	GitHub Foundations GitHub Actions
Available Languages:	Portuguese (Brazil), Korean, Spanish, English, Japanese
Real Exam Qty:	65
Exam Price:	$99 USD
Certificate Validity Period:	1 year (renewable)
Exam Format:	Multiple-choice, Scenario-based
Passing Score:	700 / 1000
Exam Duration:	100 minutes
Sample Questions:	Microsoft GH-300 Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles.
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

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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	GitHub Copilot Plans and Feature: This domain targets Product Managers and DevOps Engineers and focuses on understanding the various GitHub Copilot subscription plans like Individual, Business, and Enterprise, including distinctions and management features. It covers how Copilot is integrated into IDEs, different triggering methods for code suggestions, organizational policy management, subscription administration via API, and effective use of Copilot Chat and Knowledge Bases. Candidates also learn about CLI usage and configuration.
Topic 3	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.

Microsoft GitHub Copilot Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 relies on the model's training data to generate responses.
C. It primarily uses search results from Bing to generate responses.
D. It combines its training data set, code in user repositories, and external sources like Bing to generate responses.

Answer: D

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 # 23
You use GitHub Copilot Chat to debug a function.
You discover that GitHub Copilot is using irrelevant chat threads to generate output.
You need to ensure that GitHub Copilot only uses new input to generate output.
What should you do, and how will this affect the prompt process flow?

A. Start a new chat in GitHub Copilot Chat; the previous chat history will not be included in the new model input.
B. Start a new chat in GitHub Copilot Chat; the previous chat history will remain included in the model input.
C. Continue the existing chat; the previous chat history will be removed from the model input.
D. Continue the existing chat; the previous chat history will remain included in the model input.

Answer: A

Explanation:
GitHub Copilot Chat uses conversation history as part of the context for later responses. GitHub ' s prompt- engineering guidance recommends keeping chat history relevant, while GitHub ' s AI-usage guidance specifically advises starting a new conversation when moving to unrelated work because a long thread carries prior history into subsequent requests. In this scenario, irrelevant earlier chat threads are contaminating the context used for debugging. Starting a new Copilot Chat session creates a clean conversational context for the new prompt, so the previous thread is no longer included as model input for that new conversation.
Continuing in the same chat would preserve the unwanted context. Therefore, the correct action is to start a new chat, with the effect of removing the prior chat history from the new model input.

NEW QUESTION # 24
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?

A. Inclusiveness
B. Fairness
C. Transparency
D. Accountability

Answer: C

Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrives at its decisions and suggestions.

NEW QUESTION # 25
Select a strategy to increase the performance of GitHub Copilot Chat.

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

Answer: D

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 # 26
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. Real-time user input helps generate context-aware code suggestions.
B. Prompt data is stored unencrypted for faster processing.
C. Prompt data is used internally by GitHub for improving the search engine.
D. Prompt data is used to train machine learning models for better code suggestions.

Answer: A,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 # 27
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