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Exam Code: Databricks-Certified-Data-Engineer-Associate
Exam Name: Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版)
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
Topic 2
  • Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI
  • CD workflows.
Topic 3
  • Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
Topic 4
  • ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
Topic 5
  • Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.

Reference: https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版) Sample Questions:

データエンジニアは、パフォーマンスのボトルネックが頻繁に発生するPySparkジョブに基づくETLパイプラインを調査しています。開発者からのフィードバックに基づき、データエンジニアはジョブの計算リソースが不足していると推測します。問題を特定するために、データエンジニアはSpark UIを監視したところ、タスク時間に対するCPU時間の割合が高いことがわかりました。データエンジニアはどのような対応を取るべきでしょうか？

  • A. CPU時間とタスク時間の比較が高いということは、メモリが過剰に使用されていることを意味し、並列処理を増やす必要がある。
  • B. CPU時間とタスク時間のバランスが悪いということは、クラスターが十分に活用されていないことを意味します。データエンジニアは、クラスター全体にジョブをより均等に分散させるために、データの再パーティション化が必要になる場合があります。
  • C. CPU時間とタスク時間の比較で高い値を示す場合、CPUが過剰に使用されているジョブであることを意味します。データエンジニアは、エグゼキュータとコアのチューニング、またはクラスタのサイズ変更を検討する必要があるかもしれません。
  • D. CPU時間とタスク時間の比較が高いということは、クラスターを効率的に使用しており、変更は不要であることを意味します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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データアナリストが、データ分析チーム全体で使用するDeltaテーブルsalesを作成しました。データエンジニアリングチームに協力を求め、データのクリーン性を確認するための一連のテストを実施したいと考えています。しかし、データエンジニアリングチームはテストにSQLではなくPythonを使用しています。
データ エンジニアリング チームが PySpark で売上にアクセスするために使用できるコマンドはどれですか。

  • A. spark.delta.table("sales")
  • B. spark.sql("sales")
  • C. spark.table("sales")
  • D. PySpark と SQL 間でデータを共有する方法はありません。
  • E. SELECT * FROM sales
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

ノートブックが失敗しないようにするために、ジョブクラスタがサポートする最大出力はどれくらいですか？

  • A. 15MB
  • B. 10MB
  • C. 25MB
  • D. 30MB
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for 2Pass4sure members. You can sign-up / login (it's free).

データエンジニアは、クラウドストレージに毎時間アップロードされる数千もの新しいJSONファイルを段階的にロードする必要があります。このソリューションは、実行ごとにディレクトリ全体を一覧表示することなく、既に処理済みのファイルを追跡し、将来的には数百万ものファイルに対応できる拡張性を備えている必要があります。
エンジニアはどちらのアプローチを採用すべきか？

  • A. ディレクトリ全体に対して spark.read.json() を使用し、1 時間ごとにターゲット テーブルを上書きします。
  • B. cloudFiles.formatをjsonに設定し、ファイル状態追跡のためのチェックポイントの場所を指定してAuto Loaderを使用します。
  • C. 手動で管理されているファイルパスのリストから、毎晩 MERGE INTO を実行します。
  • D. COPY INTO を FORCE = true で使用して、実行ごとにすべてのファイルを再処理します。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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データアナリストがDeltaテーブルに対して実行されるクエリを開発しました。クエリによって返されるデータがクリーンであることを確認するための一連のテストを実装するために、データエンジニアリングチームの協力を求めています。
しかし、データエンジニアリングチームはテストにSQLではなくPythonを使用している。
データエンジニアリングチームは、PySparkでクエリを実行し、結果を操作するために、次のうちどの操作を使用できますか？

  • A. spark.delta.table
  • B. PySparkとSQL間でデータを共有する方法はありません。
  • C. spark.sql
  • D. SELECT * FROM sales
  • E. spark.table
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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