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Microsoft GH-300 Exam Overview:

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

주제소개
주제 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.
주제 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.
주제 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.
주제 4
  • 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.
주제 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.
주제 6
  • 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.

최신 GitHub Administrator GH-300 무료샘플문제 (Q78-Q83):

질문 # 78
What should developers consider when relying on GitHub Copilot for generating code that involves statistical analysis?

정답：B

설명：
Developers should consider that GitHub Copilot's suggestions are based on statistical trends and may not always be accurate for specific datasets, requiring careful validation.
Reference: GitHub Copilot data analysis limitations.


질문 # 79
What is a likely effect of GitHub Copilot being trained on commonly used code patterns?

정답：B

설명：
"Because GitHub Copilot is trained on a large amount of publicly available code, it often suggests snippets that reflect common coding practices and idioms." This shows that Copilot reflects the most common practices it has seen in its training data.
References: GitHub Copilot documentation on training and suggestion patterns.


질문 # 80
What are the additional checks that need to pass before the GitHub Copilot responses are submitted to the user? (Each correct answer presents part of the solution. Choose two.)

정답：B,C

설명：
Exact extracts:
* "Copilot applies post-processing checks for code quality to ensure that responses meet baseline standards." References: GitHub Copilot safeguards documentation.


질문 # 81
Identify the steps involved in the life cycle of a GitHub Copilot code suggestion? (Each correct answer presents part of the solution. Choose two.)

정답：B,C

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


질문 # 82
Which of the following describes role prompting?

정답：A

설명：
Role prompting involves explicitly stating your role or the persona you want GitHub Copilot to adopt within your prompt. This helps Copilot provide more contextually relevant and accurate suggestions. By defining your role (e.g., "As a senior software engineer," "As a technical writer"), you guide Copilot to tailor its responses to align with the expertise and perspective associated with that role. This improves the quality and relevance of the generated code and explanations.


질문 # 83
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