Microsoft GH-300日本語 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.

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

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

Question #1

統計分析を伴うコードを生成するために GitHub Copilot を利用する場合、開発者はどのような点を考慮すべきでしょうか?

A. GitHub Copilot は、ユーザーの初期コードで見つかった統計エラーを自動的に修正します。
B. GitHub Copilot は、結果の統計的有意性を独自に検証できます。
C. GitHub Copilot は、これまで文書化されていなかった新しい統計手法を設計できます。
D. GitHub Copilot の提案は統計的な傾向に基づいており、特定のデータセットに必ずしも正確に適用されるとは限りません。

Reveal Solution Discussion 0

Correct Answer: D 🗳️