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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:
| Section | Objectives |
| Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture
- Medallion architecture (Bronze, Silver, Gold)
- Data governance concepts (Unity Catalog basics)
|
| Data Ingestion and Processing | - Structured Streaming fundamentals
- ETL pipeline design patterns
- Batch and streaming ingestion with Auto Loader
|
| Production Pipelines and Orchestration | - Error handling and recovery strategies
- Databricks Workflows
- Job scheduling and monitoring
|
| Data Modeling and Transformation | - Dimensional modeling concepts
- Performance optimization techniques
- Spark SQL transformations
|
| Delta Lake and Data Management | - Delta Lake transactions and ACID properties
- Time travel and versioning
- Schema evolution and enforcement
|
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:
Question 1
データエンジニアがメールアドレスを含む列をマスクしています。目標は、すべての行で同じ長さの出力文字列を生成し、メールアドレスの値ごとに異なる出力を生成することです。
これを実現するにはどの SQL 関数を使用する必要がありますか?
A. mask(email, '?')
B. sha1(email)
C. sha2(email, 0)
D. hash(email)
Question 2
新人データエンジニアが、KafkaソースをDelta Lakeに書き込むアプリケーションで重要なフィールドが省略されていることに気づきました。この重要なフィールドはKafkaソース内に存在していたにもかかわらず、この問題が発生しました。
このフィールドは、依存する長期ストレージに書き込まれたデータからも欠落していました。Kafkaサービスの保持期間のしきい値は7日間です。パイプラインは3か月間本番環境で稼働しています。
将来的にこのようなデータ損失を回避するために Delta Lake がどのように役立つかを説明していますか?
A. Delta Lake スキーマの進化により、データが元のソース内にある限り、新しく追加されたフィールドの正しい値を遡及的に計算できます。
B. Delta Lake からデータが完全に削除されることはありません。そのため、どのような状況でもデータの損失は発生しません。
C. Kafka からブロンズ Delta テーブルにすべての生データとメタデータを取り込むと、データ状態の永続的で再生可能な履歴が作成されます。
D. Delta ログと構造化ストリーミング チェックポイントには、Kafka プロデューサーの完全な履歴が記録されます。
E. Delta Lake は、ソース データに存在するすべてのフィールドが取り込みレイヤーに含まれているかどうかを自動的に確認します。
Question 3
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-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
C. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
Question 4
データチームは、バッチデータとストリーミングデータの両方を処理する、追加専用のDelta Lakeパイプラインを実装しています。ソースデータのスキーマ変更がパイプラインを中断することなく自動的に反映されるようにしたいと考えています。Deltaテーブルにデータを書き込む際、どのような構成を使用すべきでしょうか?
A. 検証スキーマ = false
B. overwriteSchema = true
C. ignoreChanges = false
D. mergeSchema = true
Question 5
トランザクションテーブルは、product_id、user_id、event_date列に基づいてリキッドクラスター化されています。どの操作がクラスターオンライトをサポートしていませんか?
A. spark.writestream.format('delta').mode('append')
B. spark.write.format('delta').mode('append')
C. CTAS and RTAS statements
D. INSERT INTO operations
Solutions:
Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: A |