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

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Microsoft GH-300 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • Developer Use Cases for AI: Targeting Software Engineers and Technical Leads, this domain elaborates on how AI improves developer productivity across common tasks like learning new languages, translation, documentation, debugging, data science, and refactoring. It discusses Copilot’s support in software development lifecycle management and highlights its limitations. Use of the productivity API to track Copilot’s impact is also included.
Topic 5
  • 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 6
  • 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 (Q107-Q112):

NEW QUESTION # 107
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

Answer: A,C

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


NEW QUESTION # 108
How can you use GitHub Copilot to get inline suggestions for refactoring your code? (Select two.)

Answer: B,C

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 # 109
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

Answer: A,C

Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.


NEW QUESTION # 110
How can GitHub Copilot facilitate a smoother learning experience when diving into a new programming language? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D

Explanation:
GitHub Copilot helps with learning new languages by providing guidance on common tasks and by converting comments into code, allowing developers to see syntax in action.
Reference: GitHub Copilot language learning documentation.


NEW QUESTION # 111
What is the process behind identifying public code matches when using a public code filter enabled in GitHub Copilot?

Answer: A

Explanation:
When the public code filter is enabled, GitHub Copilot runs code suggestions through filters designed to detect matches with publicly available code. This helps prevent the generation of code that might infringe on copyright or licensing agreements.
Reference: GitHub Copilot documentation on public code filtering and licensing.


NEW QUESTION # 112
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