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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
Related Certifications:	GitHub Copilot Fundamentals Part 1 GitHub Copilot Fundamentals Part 2
Certificate Validity Period:	2 years
Exam Price:	USD 99
Real Exam Qty:	60-75 (approximately 65 scored + unscored items)
Exam Duration:	100-120
Exam Format:	Drag and drop, Scenario-based questions, Multiple choice, Multiple response
Available Languages:	Portuguese (Brazil), English, Korean, Japanese, Spanish
Recommended Training:	GitHub Copilot Fundamentals Part 1 GitHub Copilot Fundamentals Part 2
Exam Registration:	Microsoft Learn Certification Page Pearson VUE Scheduling
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/

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Microsoft GH-300 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Microsoft GitHub Copilot Sample Questions (Q90-Q95):

NEW QUESTION # 90
Select a strategy to increase the performance of GitHub Copilot Chat.

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

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 # 91
What role does chat history play in GitHub Copilot's code suggestions?

A. Chat history is used to train the GitHub Copilot model in real-time.
B. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.
C. Chat history is stored and shared with other users to enhance collaboration.
D. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.

Answer: B

Explanation:
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.
Reference: GitHub Copilot Chat history documentation.

NEW QUESTION # 92
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

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

Answer: C,D

Explanation:
Improving prompts involves providing specific success criteria and including links to supporting documentation to give GitHub Copilot more context and direction.
Reference: GitHub Copilot prompt engineering best practices.

NEW QUESTION # 93
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. Extensive support for all programming languages
C. No biases in code suggestions
D. Limited training data

Answer: A,D

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 # 94
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

A. By running the gh copilot fix command.
B. By adding comments to your code and triggering a suggestion.
C. By highlighting the code you want to fix, right-clicking, and selecting "Fix using GitHub Copilot."
D. By highlighting the code you want to fix, right-clicking, and selecting "Refactor using GitHub Copilot."
E. By using the "/fix" command in GitHub Copilot in-line chat.

Answer: B,D

Explanation:
You can use GitHub Copilot for inline refactoring suggestions by adding comments to your code to trigger suggestions and by highlighting the code and selecting "Refactor using GitHub Copilot" from the context menu.

NEW QUESTION # 95
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