Databricks : Databricks-Certified-Data-Engineer-Professional日本語 Questions & Answers
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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources |
| Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Data Governance | 7% | - Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata |
| Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:
Question 1
Delta Lake の最適化された書き込みについて説明している記述はどれですか。
A. 書き込み前にシャッフルが発生し、データをグループ化しようとするため、各実行者がディレクトリ パーティションに基づいて複数のファイルを書き込む代わりに、ファイル数が減ります。
B. 書き込みが完了した後、ファイルをさらに圧縮できるかどうかを検出するための非同期ジョブが実行されます。はい、デフォルトの 1 GB に向けて OPTIMIZE ジョブが実行されます。
C. ジョブ クラスターが終了する前に、最新のジョブ中に変更されたすべてのテーブルに対して OPTIMIZE が実行されます。
D. 最適化された書き込みでは、ディレクトリ パーティションではなく論理パーティションが使用され、パーティション境界はメタデータでのみ表され、書き込まれる小さなファイルが少なくなります。
Question 2
データエンジニアは、Unity Catalog で管理されるテーブル内の機密性の高い列に列マスキングを実装する必要があります。マスキングロジックでは、ユーザーが特定のグループに属しているかどうかを動的にチェックする必要があります。このグループは、グループを許可された部門にマッピングする別のテーブル(group_access)で定義されています。この要件を効率的に適用するには、エンジニアはどのようなアプローチを採用すべきでしょうか？
A. UDF 内の group_access マッピング テーブルを参照する列マスクを適用します。
B. 機密列を選択せずにビューを作成します。
C. 許可されたグループをハードコードする UDF を作成し、それを列マスクとして適用します。
D. 行フィルターを使用して、ユーザーのグループに基づいてアクセスを制限します。
Question 3
大規模なデータセットを扱うパフォーマンスが重要なアプリケーションでは、従来の PySpark UDF よりも Pandas UDF が好まれることが多いのはなぜでしょうか?
A. Apache Arrow を活用して、JVM と Python ランタイム間のベクトル化された操作を可能にし、シリアル化コストを削減し、計算効率を向上させます。
B. シリアル化を完全にバイパスすることで JVM と Python の境界を排除し、データ変換のオーバーヘッドを回避します。
C. ネイティブ Spark 最適化を使用して Python で関数の行レベルの実行が可能になり、列実行の必要性がなくなります。
D. 軽量の Python ラッパーを介して各行を個別にストリーミングすることで、メモリ使用量を最小限に抑え、バッチ処理のオーバーヘッドを回避します。
Question 4
データエンジニアは、各銘柄グループ内の行間で状態を維持する必要がある複雑な計算を伴う金融時系列データを処理するPandas UDFを設計する際に、関数の効率性とスケーラビリティを確保する必要があります。データの整合性を維持しながら、最小限のオーバーヘッドでこの問題を解決できるアプローチはどれでしょうか？
A. データセット全体を一度に処理する SCALAR Pandas UDF を使用し、UDF 内にカスタム パーティション ロジックを実装して銘柄コード別にグループ化し、すべての実行プロセスで共有されるグローバル変数を使用して状態を維持します。
B. 各株価シンボル グループを個別に処理し、ブロードキャスト変数を介して連続する UDF 呼び出し間で渡される中間集計結果を通じて状態を維持する grouped_agg Pandas UDF を使用します。
C. 各銘柄のすべての行を Pandas DataFrame として受け取る Spark DataFrame で applyInPandas() を使用し、各グループの処理関数に対してローカルな状態変数を維持しながら、各グループ内での処理を可能にします。
D. 反復子ベースの処理で SCALAR_ITER Pandas UDF を使用し、反復子チャンク間の継続性を維持するためにバッチごとに更新される永続ストレージ (デルタ テーブル) を通じて状態管理を実装します。
Question 5
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？
A. イベントを順序付けるために sequence_by を使用し、古いシーケンスが処理されるまで削除された行の tombstone を保持します。
B. 同じキーの更新後に削除が行われた場合は無視されます。
C. ターゲット テーブルで VACUUM を実行し、競合するレコードを消去します。
D. 変更を適用する前に、受信イベントをタイムスタンプで手動で並べ替えます。
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: A | Question 4 Answer: C | Question 5 Answer: A |
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