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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 - Improve query and pipeline performance - Optimize compute and storage resources |
| Topic 2: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
| Topic 4: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Topic 5: Data Governance | 7% | - Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata |
| Topic 6: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic |
| Topic 7: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Topic 8: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Topic 9: Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Topic 10: 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:
1. データ エンジニアは、遅れて到着する重複レコードが表示される可能性があるパイプラインを構成しています。
バッチ内のレコードの重複排除に加えて、デルタ テーブルに挿入されるときに、データ エンジニアが以前に処理されたレコードに対してデータの重複排除を実行できるアプローチは次のどれですか。
A) 一意のキーで完全外部結合を実行し、既存のデータを上書きします。
B) Delta Lake スキーマの適用を利用して、重複レコードを防止します。
C) 一意のキーに対して一致する条件を使用して挿入のみのマージを実行します。
D) 各バッチが完了した後、Delta テーブルを VACUUM します。
E) 構成 delta.deduplicate = true を設定します。
2. チームのジュニア データ エンジニアが次のコード ブロックを実装しました。
new_eventsビューには、events Deltaテーブルと同じスキーマを持つレコードのバッチが含まれています。event_idフィールドは、このテーブルの一意のキーとして機能します。
このクエリを実行すると、既存のレコードと同じ event_id を持つ新しいレコードはどうなるでしょうか?
A) 更新されました。
B) 削除されます。
C) 無視されます。
D) 結合されます。
E) 挿入されます。
3. デルタ レイクとレイクハウスについて正しいのは次のうちどれですか?
A) Z オーダーは Delta Lake テーブルに格納されている数値にのみ適用できます。
B) Delta Lake は、クエリ フィルターに基づいてデータのスキップに活用される各テーブルの最初の 32 列の統計を自動的に収集します。
C) Lakehouse 内のビューは、常にソース テーブルの最新バージョンの有効なキャッシュを維持します。
D) Parquet はデータを行ごとに圧縮するため、文字列は文字が複数回繰り返される場合にのみ圧縮されます。
E) 主キー制約と外部キー制約を利用すると、ディメンション テーブルに重複する値が入力されないようにすることができます。
4. データエンジニアが、customer_id、transaction_timestamp(ミリ秒単位の精度)、amount_spent を含む PySpark DataFrame df 内のトランザクションデータを分析しています。目的は、顧客ごとに、transaction_timestamp の順序に従って、amount_spent の累積合計を計算することです。累積合計には、最も古いタイムスタンプから現在の行までのすべてのトランザクションが含まれ、各顧客パーティション内の時間的な順序が尊重される必要があります。適切なウィンドウ指定を最も正確に構築し、集計を適用して顧客ごとの正確な累積支出を算出する PySpark コードスニペットはどれですか。
A)
B)
C)
D) 
5. データ エンジニアが次の PySpark コードを実行しようとしています。
df = spark.read.table("売上")
結果 = df.groupBy("地域").agg(合計("収益"))
しかし、実行プランを調べて Spark ジョブをプロファイリングすると、集約フェーズで過度のデータ シャッフルが発生していることがわかります。
groupBy 集計操作中のシャッフルを減らすには、どの手法を適用する必要がありますか?
A) 集約後に coalesce() を使用します。
B) DataFrame df をキャッシュします。
C) ブロードキャスト参加を使用します。
D) 集約前にリージョンごとに再パーティション化します。
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: D |
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