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| Section | Objectives |
|---|---|
| Data Modeling and Transformation | - Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations |
| Delta Lake and Data Management | - Delta Lake transactions and ACID properties - Schema evolution and enforcement - Time travel and versioning |
| Data Ingestion and Processing | - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns |
| Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) |
| Production Pipelines and Orchestration | - Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies |
The Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) is one of Databricks's influential certification exams — pass it and you earn the Databricks Certified Data Engineer Professional certification (Professional level). Among IT workers it's a popular way to prove professional knowledge in the field. It also connects with related credentials such as Databricks Certified Data Engineer Associate.
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The official Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) blueprint contains 5 domains, including Data Modeling and Transformation, Production Pipelines and Orchestration, Data Ingestion and Processing. The weightings tell you where the exam's points live — allocate your spare time to match. The complete outline above has every domain and subtopic.
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データエンジニアは、Lakeflow Spark Declarative Pipeline の AUTO CDC API を使用して、ソーステーブル (orders_source) からターゲットテーブル (orders_target) への削除処理を伝播させています。ソーステーブルでは変更データフィード (CDF) が有効になっていますが、上流の遅延により、一部の削除イベントが順序どおりに配信されません。AUTO CDC API は、順序どおりに配信されないイベントが発生しても、削除処理が正しく適用されることを内部的にどのように保証しているのでしょうか?
Correct Answer: D 🗳️
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データサイエンスチームはMLflowを使用して本番環境モデルを作成し、ログに記録しました。以下のコードは、本番環境モデルを正しくインポートして適用し、予測結果を「customer_id LONG, predictions DOUBLE, date DATE」というスキーマを持つpredsという名前の新しいデータフレームとして出力します。
データサイエンスチームは、予測結果をDelta Lakeテーブルに保存し、すべての予測を時系列で比較できるようにしたいと考えています。チャーン予測は1日に最大1回まで行われます。
潜在的な計算コストを最小限に抑えながらこのタスクを実行するコード ブロックはどれですか。



Correct Answer: B 🗳️
データエンジニアは、Pythonライブラリを介してOpen AIを呼び出す不正検出パイプラインを構築しており、APIを使用する際にアクセストークンを含める必要があります。データエンジニアは、シークレットを作成するためにどのDatabricks CLIコマンドを使用すればよいでしょうか?
Correct Answer: C 🗳️
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ビューの更新は、顧客テーブルに挿入または更新される新しく取り込まれたすべてのデータの増分バッチを表します。
これらのレコードを処理するには、次のロジックが使用されます。
この実装について説明している記述はどれですか?
Correct Answer: C 🗳️
ジュニアデータエンジニアが、Delta Lakeの変更データフィード機能を利用して、delta.enableChangeDataFeed = trueプロパティで作成されたブロンズテーブルの全行で有効であったすべての値を表すType 1テーブルを作成しようとしています。彼らは、以下のコードを日常的なジョブとして実行する予定です。
上記のクエリを複数回実行した場合の実行と結果を説明するステートメントはどれですか?
Correct Answer: E 🗳️
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