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Microsoft GH-300 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:GitHub Copilot Exam (GH-300)
Exam Number:GH-300
Exam Format:Multiple response, Drag and drop, Multiple choice, Scenario-based questions
Certificate Validity Period:2 years
Real Exam Qty:60-75 (approximately 65 scored + unscored items)
Passing Score:700/1000
Exam Duration:100-120
Related Certifications:GitHub Copilot Fundamentals Part 2
GitHub Copilot Fundamentals Part 1
Available Languages:English, Portuguese (Brazil), Spanish, Japanese, Korean
Exam Price:USD 99
Recommended Training:GitHub Copilot Fundamentals Part 1
GitHub Copilot Fundamentals Part 2
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
  • 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
  • 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
  • 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 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
  • 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 6
  • 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.

Microsoft GitHub Copilot Sample Questions (Q44-Q49):

NEW QUESTION # 44
What are the potential limitations of GitHub Copilot in maintaining existing codebases?

Answer: D

Explanation:
"Copilot may not always understand the full context or complex dependencies in large codebases, and suggestions may require significant review." This proves that the main limitation is a lack of deep awareness of complex or large codebase contexts.
References: GitHub Copilot limitations documentation.


NEW QUESTION # 45
Identify the steps involved in the life cycle of a GitHub Copilot code suggestion? (Each correct answer presents part of the solution. Choose two.)

Answer: B,E

Explanation:
Exact extracts:
* "Copilot first captures the context of the developer's code and environment." References: GitHub Copilot technical overview documentation.


NEW QUESTION # 46
Select a strategy to increase the performance of GitHub Copilot Chat.

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 # 47
Which of the following are true about code suggestions? (Each correct answer presents part of the solution.
Choose two.)

Answer: C,E

Explanation:
Exact extracts:
* "You can accept individual words, lines, or entire suggestions with keyboard shortcuts." References: GitHub Copilot editor usage documentation.


NEW QUESTION # 48
A social media manager wants to use AI to filter content. How can they promote transparency in the platform' s AI operations?

Answer: C

Explanation:
Exact extracts:
* "Transparency. AI systems should be understandable."
References: Microsoft Responsible AI guidelines and transparency notes.


NEW QUESTION # 49
......

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