Databricks-Certified-Data-Engineer-Associate日本語 study materials - Databricks Databricks-Certified-Data-Engineer-Associate日本語 dumps VCE

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

Exam Code: Databricks-Certified-Data-Engineer-Associate-JPN

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

Updated: Oct 24, 2026

Q & A: 322 Questions and Answers

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Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.
Topic 5
  • 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.

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:

Question #1

次のシナリオのうち、データ エンジニアが INSERT INTO コマンドの代わりに MERGE INTO コマンドを使用する必要があるのはどれですか。

  • A. 対象テーブルに重複レコードを含めることができない場合
  • B. ソーステーブルを削除できる場合
  • C. データの場所を変更する必要がある場合
  • D. 対象テーブルが外部テーブルの場合
  • E. ソースがデルタテーブルではない場合
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

データエンジニアが、運用上の負担を軽減するためにパイプラインタスクを移行しています。ワークスペースはUnity Catalogを使用しており、サーバーレスをサポートするリージョンにあります。エンジニアは、Databricksがインスタンスタイプの自動選択、スケーリングの管理、Photonの適用、およびジョブ実行時のランタイムアップグレードの自動処理を行うことを望んでいます。
データエンジニアは、Databricksの制約を遵守しながら、この要件をどのように満たすべきでしょうか?

  • A. 単一タスクのジョブクラスタを使用してジョブを作成し、インスタンスファミリーと最小/最大ワーカーを手動で設定します。
  • B. ワークフロー構成のためにサーバーレスコンピューティングでジョブを実行し、Unity Catalogが有効になっていることと、リージョンサポートが利用可能であることを確認してください。
  • C. 標準サイズを適用し、オートスケーリングを有効にするクラスタポリシーを備えた汎用クラスタを使用します。
  • D. Pro SQLウェアハウスを使用し、Pythonノートブックタスクをパイプラインステップとして実行するようにスケジュールします。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

データエンジニアは、SalesforceからJSON形式の変更データを、ローコードかつフルマネージドな環境でUnityカタログで管理されるDeltaテーブルに取り込む必要があります。このデータエンジニアは、Databricksのどの機能を使用すべきでしょうか?

  • A. Unity Catalog Deltaテーブルに書き込むLakeflow Connect管理型Salesforceコネクタ
  • B. DBFS上のファイル到着をトリガーとする自動ローダー
  • C. PySparkノートブックでSalesforce REST APIを使用して変更されたレコードをクエリし、Deltaテーブルに書き込みます。
  • D. SalesforceがエクスポートしたS3バケットからJSONファイルを読み込むオートローダー
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

データエンジニアは、Databricks Notebooksに組み込まれているバージョン管理機能を使用するか、Databricks Reposを使用してプロジェクトのバージョン管理を行うかを決定する必要があります。
Databricks Reposを使用する利点として、Databricks Notebooksのバージョン管理と比較した場合、次のうちどれが挙げられますか?

  • A. Databricks Reposでは、特定の変更に対してコメントする機能を提供しています。
  • B. Databricks ReposはDatabricks Data Intelligence Platform内に完全に格納されています
  • C. Databricks Reposは開発の進捗状況を自動的に保存します
  • D. Databricks Reposを使用すると、ノートブックの以前のバージョンに戻すことができます。
  • E. Databricks Reposは複数のブランチの使用をサポートしています
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

Question #5

SQL を使用して Delta Live Tables (DLT) テーブルを作成するときに、CREATE LIVE TABLE 構文ではなく CREATE STREAMING LIVE TABLE (以前の CREATE INCREMENTAL LIVE TABLE) 構文を使用する必要があるのはどれですか。

  • A. DLT パイプラインの後続のステップが静的である場合は、CREATE STREAMING LIVE TABLE を使用する必要があります。
  • B. データを増分的に処理する必要がある場合は、CREATE STREAMING LIVE TABLE を使用する必要があります。
  • C. 複雑な集計を通じてデータを処理する必要がある場合は、CREATE STREAMING LIVE TABLE を使用する必要があります。
  • D. DLT パイプラインの前のステップが静的である場合は、CREATE STREAMING LIVE TABLE を使用する必要があります。
  • E. CREATE STREAMING LIVE TABLE は DLT では冗長なので、使用する必要はありません。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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