Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) : Databricks-Certified-Data-Engineer-Professional日本語
- Exam Code: Databricks-Certified-Data-Engineer-Professional日本語
- Exam Name: Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版)
- Updated: Oct 09, 2026
- Q & A: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Topic 2: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines |
| Topic 3: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code |
| Topic 4: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Topic 5: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Topic 6: Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
| Topic 7: Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
| Topic 8: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 9: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Topic 10: Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:
データ エンジニアは、静的な userlookup テーブルの値をストリーミング DataFrame streamingDF と組み合わせる結合操作を実行しています。
無効なストリーム静的結合を実行しようとするコード ブロックはどれですか。
- A. userLookup.join(streamingDF, ["user_id"], how="right")
- B. userLookup.join(streamingDF, ["userid"], how="inner")
- C. streamingDF.join(userLookup, ["user_id"], how="outer")
- D. streamingDF.join(userLookup, ["userid"], how="inner")
- E. streamingDF.join(userLookup, ["user_id"], how="left")
Correct Answer: C 🗳️
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ユーザーからのコンテンツ投稿に関するメタデータを表す Delta Lake テーブルのスキーマは次のとおりです。
user_id LONG、post_text STRING、post_id STRING、経度 FLOAT、
緯度 FLOAT、投稿時刻 TIMESTAMP、日付 DATE
このテーブルは日付列でパーティション分割されています。次のフィルターを使用してクエリを実行します。
経度 < 20 & 経度 > -20
データがどのようにフィルタリングされるかを説明した記述はどれですか?
- A. デルタ ログの統計は、フィルター処理された範囲内のレコードを含む可能性のあるデータ ファイルを識別するために使用されます。
- B. Delta Engine は parquet ファイルのフッターをスキャンして、フィルター条件を満たす各行を識別します。
- C. Delta Engine は、トランザクション ログ内の行レベルの統計を使用して、フィルター条件を満たすフライを識別します。
- D. デルタ ログの統計は、フィルターされた範囲内のファイルが含まれる可能性のあるパーティションを識別するために使用されます。
- E. オプティマイザーはパーティション列と経度の関係を認識していないため、ファイルのスキップは発生しません。
Correct Answer: A 🗳️
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データエンジニアリングチームのジュニアメンバーが、Databricksノートブックの言語相互運用性について調査しています。以下のコードの目的は、geo_lookupテーブルに含まれるアフリカ大陸の国々で発生したすべての売上のビューを登録することです。
コードを実行する前に、現在のデータベースで SHOW TABLES を実行すると、データベースには geo_lookup と sales の 2 つのテーブルのみが含まれていることがわかります。
インタラクティブ ノートブックでこれらのコマンド セルを順番に実行した結果を正しく説明している記述はどれですか。
- A. Cmd 1 は成功し、Cmd 2 は失敗します。countries at は文字列のリストを含む Python 変数になります。
- B. どちらのコマンドも失敗します。新しい変数、テーブル、ビューは作成されません。
- C. コマンド1は成功します。コマンド2は、アクセス可能なすべてのデータベースでcountries afという名前のテーブルまたはビューを検索します。このエンティティが存在する場合、コマンド2は成功します。
- D. どちらのコマンドも成功します。show tablesを実行すると、countries atとsales atがビューとして登録されていることが確認できます。
- E. Cmd 1 は成功し、Cmd 2 は失敗します。countries at は PySpark DataFrame を表す Python 変数になります。
Correct Answer: A 🗳️
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データエンジニアとプラットフォームエンジニアが協力してシステムタスクの自動化に取り組んでいます。特定の日次Databricksジョブがその日のうちに正常に完了した場合にのみ、Databricksの外部でスクリプトを実行する必要があります。ジョブの最終実行を確認するために、Databricks CLIコマンドを使用しました。このタスクに必要なコマンドオプションは何ですか？
- A. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
- B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
- C. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
- D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
Correct Answer: A 🗳️
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データ エンジニアは、非常に類似したコードを持つ複数のテーブル定義を含む次の DLT コードをリファクタリングしたいと考えています。
パラメーター化されたテーブル定義を使用してこれらのテーブルをプログラムで作成するために、データ エンジニアは次のコードを記述します。
パイプラインは、このリファクタリングされたコードを使用して更新を実行しますが、これらのテーブルの誤った構成値を示す別の DAG を生成します。
データエンジニアはこれをどうやって修正できるでしょうか?
- A. パイプライン パラメータによって指定されたパスにある別のファイルからこれらのテーブルの構成値を読み込みます。
- B. for ループの異なる入力を使用して、構成値のリストをテーブル設定の辞書に変換します。
- C. ループを別のテーブル定義内にラップし、一般化された名前とプロパティを使用して、内部テーブルのものと置き換えます。
- D. テーブル名をキーとして使用して、構成値のリストをテーブル設定の辞書に変換します。
Correct Answer: D 🗳️
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