Exam Code: GH-300
Exam Name: GitHub Copilot (GH-300日本語版)
Certification Provider: Microsoft
Corresponding Certification: GitHub Administrator

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Microsoft GH-300日本語 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	GitHub Copilot
Exam Number:	GH-300
Available Languages:	Korean, English, Japanese, Portuguese (Brazil), Spanish
Real Exam Qty:	65
Related Certifications:	GitHub Actions GitHub Foundations
Exam Format:	Scenario-based, Multiple-choice
Passing Score:	700 / 1000
Exam Price:	$99 USD
Exam Duration:	100 minutes
Certificate Validity Period:	1 year (renewable)
Sample Questions:	Microsoft GH-300日本語 Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	No formal prerequisites. Microsoft recommends practical experience using GitHub Copilot in an IDE and a general understanding of software development principles.
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/github-copilot

Microsoft GH-300日本語 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	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	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 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.
Topic 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.
Topic 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.

Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/GH-300

Microsoft GitHub Copilot (GH-300日本語版) Sample Questions:

Question #1

公平性の概念を AI ツールの操作プロセスにどのように統合できるでしょうか?

A. トレーニング用の大規模なデータセットの収集に重点を置くことで、公平性が確保されます。
B. アクセシビリティに重点を置くことで公平性が確保されます。
C. AI データとアルゴリズムを偏りのない状態にトレーニングすることで、公平性が確保されます。
D. AI ツールのパフォーマンスを定期的に監視することで、出力の公平性が確保されます。

Reveal Solution Discussion 0

Correct Answer: C 🗳️