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

Databricks-Certified-Data-Engineer-Associate日本語
  • 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 14, 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
  • 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 2
  • 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 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
  • 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 5
  • 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.

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

データエンジニアは、ネストされたdevicestructを含むPySpark DataFrame events_dfを持っています。devicestruct自体には、ネストされたlocationstructが含まれています。以下はその例です。

目標は、イベント識別子とタイムスタンプを保持したまま、ネストされたフィールドをルートレベルの列にフラット化することです。
これを実現するPySpark式はどれですか？

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

データエンジニアが、複数のソースシステムから同じ顧客レコードを受け取るブロンズテーブルをクリーンアップしています。重複する行は、customer_idとemailは同じですが、ingestion_timestampの値が異なります。シルバーテーブルには、customer_idとemailの一意の組み合わせごとに1つのレコードのみが含まれる必要があります。ビジネスキーに基づいて正しく重複排除を行うPySpark操作はどれですか？

  • A. df.select('customer_id', 'email').distinct()
  • B. df.groupBy('customer_id', 'email').agg(max('ingestion_timestamp').alias('latest_ts'))
  • C. df.dropDuplicates(['customer_id', 'email'])
  • D. df.distinct()
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

コード変更後、構造化ストリーミングジョブが停止され、再開されました。エンジニアは、すべてのソースデータを再処理するのではなく、ジョブが中断したところから再開されることを望んでいます。
どの構成が必要ですか？

  • A. 書き込みストリーム上で安定したチェックポイント位置を指定する
  • B. 対象テーブルでmergeSchemaを有効にする
  • C. 出力モードの設定を完了します
  • D. maxFilesPerTriggerを1に設定
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

count_if関数と、xがnullの場合のカウントがどのように使用できるかを特定してください。
以下のデータを含むテーブル random_values を考えてみましょう。
以下のクエリの出力結果はどうなりますか？
select count_if(col1 > 1) as count_a, count(*) as count_b,count(col1)
random_values から count_c として取得
col1
0
1
2
NULL
2
3

  • A. 4 6 6
  • B. 3 6 5
  • C. 4 6 5
  • D. 3 6 6
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #5

データエンジニアリングチームは、ノートブックのバージョン管理を行い、プルリクエストを通じて変更内容をレビューし、同じコードを開発環境、ステージング環境、本番環境にデプロイする必要があります。
このワークフローをサポートするDatabricksの機能はどれですか？

  • A. ノートブックの改訂履歴を有効にし、必要に応じてスナップショットを復元する
  • B. ノートブックのソースファイルをDBFS/FileStoreに保存し、手動でコピーする
  • C. ノートブックをDBCアーカイブとしてエクスポートし、ワークスペース間でメールで送信する
  • D. リモートGitプロバイダーと統合されたDatabricks Gitフォルダー(リポジトリ)
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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