# Data EngineeringのためのData Modeling (Data Engineering) > Star schema、snowflake、Data Vault、正規化、SCDタイプ、grain、加法的メトリクス - 20 面接問題 - Mid-Level - [面接問題: Data Engineering](https://sharpskill.dev/ja/technologies/data-engineering/interview-questions.md) ## 1. star schemaとは何ですか? **回答** star schemaは、中央のfact tableがdimension tableに囲まれた次元データモデルです。fact tableにはメトリクスとdimensionへのforeign keyが含まれ、dimensionには記述属性が含まれます。このシンプルで非正規化された構造は、joinを最小限に抑えることで分析クエリのパフォーマンスを最適化します。 ## 2. fact tableとdimension tableの違いは何ですか? **回答** fact tableには定量的な測定値(メトリクス)とdimensionへのforeign keyが含まれます。イベントやトランザクションを記録します。dimension tableには記述属性(誰が、何を、どこで、いつ)が含まれ、factのフィルタリングやグループ化を可能にします。factは数値で集計可能、dimensionはテキストで記述的です。 ## 3. fact tableのgrainとは何ですか? **回答** grainはfact tableの1行の詳細レベルを定義します。1行が正確に何を表すかという質問に答えます。例えば、1行に1販売、1日1製品ごとに1販売、または1時間ごとに1販売です。grainの定義は次元モデリングの最初のステップであり、どのdimensionが必要か、どのレベルの集計が保存されるかを決定します。 ## さらに17問利用可能 - star schemaとsnowflake schemaの違いは何ですか? - conformed dimensionとは何ですか? 無料で登録: https://sharpskill.dev/ja/login ## その他のData Engineering面接トピック - 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