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GH-300日本語 PDF Practice Q&A's

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Microsoft GH-300日本語 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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.
Topic 6
  • 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.

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:

GitHub Copilot の使用状況メトリクス API は、GitHub Copilot の有効性を評価するためにどのような洞察を提供できますか? (それぞれの正解は解決策の一部を示しています。2 つ選択してください。)

  • A. API は、GitHub Copilot によって行われたコード品質の改善に関する詳細なレポートを生成できます。
  • B. API を使用すると、コードをリファクタリングして生産性を向上させることができます。
  • C. API はコーディング スタイルと標準準拠に関するフィードバックを提供できます。
  • D. API は、組織内で受け入れられ、使用されたコード提案の数を追跡できます。
  • E. API は、Copilot Chat 固有の提案受け入れメトリックを提供できます。
Reveal Solution  Discussion  0

Correct Answer: D,E  🗳️

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GitHub Copilot Chat のパフォーマンスを向上させる戦略を選択します。

  • A. 生成されたコード内でメモリを大量に消費する操作の使用を最適化します
  • B. 単一のGitHub Copilot Chatクエリを使用して、技術要件のコレクションの解決策を見つけます。
  • C. より具体的にするために、迅速なエンジニアリング技術を適用します
  • D. GitHub Copilot Chat に同時アクセスできるユーザー数を制限します
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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GitHub Copilot がソフトウェア開発プロセスに与える影響を最もよく表しているのは次のどれですか。

  • A. ソフトウェア開発プロセスにおける開発者の必要性を排除します。
  • B. テストワークフローを自動化することでオーバーヘッドを削減します。
  • C. 反復的なコーディングタスクを自動化することで生産性が向上します。
  • D. サードパーティの依存関係によるソフトウェアの脆弱性を軽減します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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CLI で GitHub Copilot を使用してコード候補を生成するにはどうすればよいですか?

  • A. プロジェクトのアーキテクチャを説明します # copilot generate コマンドを使用します # 生成された提案を受け入れます。
  • B. gh copilot suggest を使用します # 必要なコマンドを記述します # リストから最適な提案を選択します。
  • C. コード スニペットを入力します。# copilot refine コマンドを使用してコードを強化します。# 提案されたコマンドを確認します。
  • D. コードコメントを記述します # 提案のショートカットを押します # リストから最適な提案を選択します。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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ソーシャルメディアマネージャーがAIを活用してコンテンツをフィルタリングしたいと考えています。プラットフォームのAI運用の透明性を高めるにはどうすればよいでしょうか?

  • A. 評判の高い AI 開発会社に依頼することで。
  • B. コンテンツ フィルタリングに対するユーザー満足度に重点を置きます。
  • C. AI がフィルタリングするように設計されたコンテンツの種類と、その結論に到達する方法について明確な説明を提供します。
  • D. AI フィルタリング アルゴリズムを定期的に更新します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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