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Microsoft GH-300 Exam Overview:
| Certification Vendor: | Microsoft |
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| Exam Name: | GitHub Copilot |
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| Exam Number: | GH-300 |
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| Exam Format: | Multiple-choice, Scenario-based |
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| Exam Price: | $99 USD |
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| Real Exam Qty: | 65 |
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| Exam Duration: | 100 minutes |
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| Available Languages: | Portuguese (Brazil), English, Spanish, Japanese, Korean |
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| Certificate Validity Period: | 1 year (renewable) |
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| Related Certifications: | GitHub Actions
GitHub Foundations |
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| Passing Score: | 700 / 1000 |
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| Sample Questions: | Microsoft GH-300 Sample Questions |
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| Exam Way: | Online proctored exam (Pearson VUE) |
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| Pre Condition: | No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles. |
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| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/github-copilot |
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Microsoft GH-300 시험요강:
| 주제 | 소개 |
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| 주제 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.
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| 주제 2 | - 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.
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| 주제 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.
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| 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.
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| 주제 5 | - 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.
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최신 GitHub Administrator GH-300 무료샘플문제 (Q63-Q68):
질문 # 63
What is the primary role of the /optimize slash command in Visual Studio?
- A. Automatically formats the code according to the selected style guide.
- B. Translates code into a more performant language.
- C. Enhances the performance of the selected code by analyzing its runtime complexity.
- D. Summarizes your documentation into more maintainable and readable formats.
정답：C
설명：
The /optimize slash command in Visual Studio enhances the performance of the selected code by analyzing its runtime complexity and suggesting improvements.
질문 # 64
Are there any limitations to consider when using GitHub Copilot for code refactoring?
- A. GitHub Copilot always produces bug-free code during refactoring.
- B. GitHub Copilot can only be used with a limited set of programming languages.
- C. GitHub Copilot may not always produce optimized or best-practice code for refactoring.
- D. GitHub Copilot understands the context of your entire project and refactors code accordingly.
정답：C
설명：
"While Copilot can suggest refactoring changes, the code may not always follow best practices or be fully optimized. Developers must review and validate suggestions." This confirms that limitations exist in optimization and best practices, making option A correct.
References: GitHub Copilot limitations documentation.
질문 # 65
In what way can GitHub Copilot and GitHub Copilot Chat aid developers in modernizing applications?
- A. GitHub Copilot can refactor applications to align with upcoming standards.
- B. GitHub Copilot can directly convert legacy applications into cloud-native architectures.
- C. GitHub Copilot can create and deploy full-stack applications based on a single query.
- D. GitHub Copilot can suggest modern programming patterns based on your code.
정답：D
설명：
GitHub Copilot and GitHub Copilot Chat are powerful AI-driven tools designed to assist developers by providing context-aware code suggestions and interactive support. Specifically, in the context of modernizing applications, GitHub Copilot excels at analyzing existing code and suggesting modern programming patterns, best practices, and syntax improvements that align with contemporary development standards. For example, it can recommend updates to outdated constructs, propose more efficient algorithms, or suggest frameworks and libraries that are widely used in modern application development.
Why not A? GitHub Copilot does not "directly convert" legacy applications into cloud-native architectures. It can assist by suggesting code changes or patterns that support such a transition, but it doesn't autonomously perform the full conversion process, which involves architectural decisions and deployment steps beyond its scope.
Why not C? While GitHub Copilot can generate code snippets and even larger portions of an application, it cannot create and deploy full-stack applications from a single query. It requires developer input, refinement, and integration to achieve a complete, deployable solution.
Why not D? GitHub Copilot can assist with refactoring by suggesting improvements to existing code, but it doesn't inherently "align with upcoming standards" in a predictive sense. Its suggestions are based on current best practices and the data it was trained on, not future standards that are yet to be defined.
Thus, B is the most accurate and realistic way GitHub Copilot aids developers in modernizing applications, leveraging its ability to provide relevant, context-based suggestions to update and improve codebases.
질문 # 66
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?
- A. Enable GitHub Copilot license checking
- B. Enforce blocking of MIT or GPL licensed code
- C. You need to check code suggestions yourself before accepting
- D. Suggestions matching public code to 'blocked'
정답：D
설명：
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.
Reference: GitHub Copilot documentation on IP protection and code filtering.
질문 # 67
What role does chat history play in GitHub Copilot's code suggestions?
- A. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.
- B. Chat history is used to train the GitHub Copilot model in real-time.
- C. Chat history is stored and shared with other users to enhance collaboration.
- D. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.
정답：D
설명：
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.
질문 # 68
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