Databricks-Certified-Data-Engineer-Professional日本語 Q&As - in .pdf

Databricks-Certified-Data-Engineer-Professional日本語 pdf
  • 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: Jul 22, 2026
  • Q & A: 250 Questions and Answers
  • PDF Price: $69.99
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  • 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)
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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:

SectionObjectives
Topic 1: Databricks Lakehouse Platform Architecture- Workspace and cluster architecture
- Data governance concepts (Unity Catalog basics)
- Medallion architecture (Bronze, Silver, Gold)
Topic 2: Delta Lake and Data Management- Delta Lake transactions and ACID properties
- Schema evolution and enforcement
- Time travel and versioning
Topic 3: Data Modeling and Transformation- Dimensional modeling concepts
- Spark SQL transformations
- Performance optimization techniques
Topic 4: Data Ingestion and Processing- Structured Streaming fundamentals
- Batch and streaming ingestion with Auto Loader
- ETL pipeline design patterns
Topic 5: Production Pipelines and Orchestration- Job scheduling and monitoring
- Databricks Workflows
- Error handling and recovery strategies

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、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) streamingDF.join(userLookup, ["user_id"], how="outer")
B) userLookup.join(streamingDF, ["user_id"], how="right")
C) streamingDF.join(userLookup, ["user_id"], how="left")
D) streamingDF.join(userLookup, ["userid"], how="inner")
E) userLookup.join(streamingDF, ["userid"], how="inner")


2. ジュニアデータエンジニアの1人が、DataFrame dfを用いたグループ化された集計機能を備えたストリーミングデータパイプラインの開発を依頼されました。このパイプラインでは、重複しない5分間隔ごとに平均湿度と平均気温を計算する必要があります。イベントはデバイスごとに1分ごとに記録されます。
ストリーミング DataFrame df には次のスキーマがあります。
「device_id INT、event_time TIMESTAMP、temp FLOAT、humidity FLOAT」
コードブロック:

このタスクを完了するには、コード ブロック内の空白を正しく埋める応答を選択してください。

A) window("event_time", "10 minutes").alias("time")
B) lag("event_time", "10 minutes").alias("time")
C) "event_time"
D) window("event_time", "5 minutes").alias("time")
E) to_interval("event_time", "5 minutes").alias("time")


3. Spark へのデータの取り込み時に Spark パーティションのサイズに直接影響する構成パラメータはどれですか?

A) spark.sql.adaptive.advisoryPartitionSizeInBytes
B) spark.sql.files.maxPartitionBytes
C) spark.sql.files.openCostInBytes
D) spark.sql.autoBroadcastJoinThreshold
E) spark.sql.adaptive.coalescePartitions.minPartitionNum


4. データエンジニアは、組織のデータ保持ポリシーを永続的に遵守するために、Databricks の Delta テーブルで削除されたファイルを(デフォルトの 7 日間ではなく)15 日間継続的に保持するようにする必要があります。削除されたファイルのこの保持期間を正しく設定するコード スニペットはどれですか。

A) spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B) from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
C) spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
D) spark.sql("VACUUM my_table RETAIN 15 HOURS")


5. ジュニア データ エンジニアが、次の JSON を Databricks REST API エンドポイント 2.0/jobs/create に投稿するワークロードを構成しました。

すべての構成と参照リソースが利用可能であると仮定した場合、このワークロードを 3 回実行した結果を説明するステートメントはどれですか。

A) 参照されているノートブックで定義されたロジックは、参照されている既存の多目的クラスターで 3 回実行されます。
B) 「新しいデータの取り込み」という名前の新しいジョブが 1 つワークスペースに定義されますが、実行されません。
C) ワークスペースに「新しいデータの取り込み」という名前の 3 つの新しいジョブが定義されますが、ジョブは実行されません。
D) 「新しいデータの取り込み」という名前の 3 つの新しいジョブがワークスペースに定義され、それぞれ 1 日 1 回実行されます。
E) 参照されたノートブックで定義されたロジックは、指定されたクラスター ID の構成を持つ新しいクラスターで 3 回実行されます。


Solutions:

Question # 1
Answer: A
Question # 2
Answer: D
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: C

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