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

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

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

Updated: Oct 29, 2026

Q & A: 118 Questions and Answers

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

TopicDetails
Topic 1
  • Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
Topic 2
  • Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 3
  • Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints
  • warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint
  • warehouses, trade-off between cluster size and cost for Databricks SQL endpoints
  • warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 4
  • SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 5
  • Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

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

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

Question #1

データアナリストがdates_dfという名前のデータフレームを扱っており、タイムスタンプフィールドから派生した新しい列「date」を追加する必要があります。
タイムスタンプから日付を抽出するには、どのコード断片を使用すればよいですか?

  • A. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()
  • B. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()
  • C. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()
  • D. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #2

あるデータアナリストは、マルチホップのメダリオンアーキテクチャを採用している組織で働いています。彼は、既存のシルバーテーブルから新しいゴールドテーブルを作成するという任務を負っています。
シルバーテーブルからゴールドテーブルへのホップを実行しているクエリはどれですか?

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

Correct Answer: C  🗳️

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Question #3

メダリオン アーキテクチャの次のレイヤーのうち、データ アナリストが最もよく使用するレイヤーはどれですか。

  • A. これらのレイヤーはいずれもデータアナリストによって使用されません
  • B. シルバー
  • C. ブロンズ
  • D. ゴールド
  • E. これらのレイヤーはすべてデータアナリストによって平等に使用されます
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

標準 SQL 方言として ANSI SQL を使用する Databricks SQL の利点は次のどれですか。

  • A. Sparkのインタープリタとの互換性が高い
  • B. Photonの計算最適化を利用できるようになります
  • C. カスタマイズ能力が向上しました
  • D. 既存のSQLクエリをDatabricks SQLに移行するのは簡単です
  • E. 他のSQL方言よりもパフォーマンスが優れています
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

データアナリストが、テーブル内に個人識別情報(PII)を含む列を発見しました。データアナリストは、このようなPIIデータにアクセスすべきではありません。
データアナリストはどのように進めるべきでしょうか?

  • A. データの操作を停止し、テーブルと既存のメタデータまたはデータファイルをすべて削除します。
  • B. データを使った作業を中止し、別のデータを使ってプロジェクトを進めてください。
  • C. 個人情報を含む列を削除し、誰にも通知せずにプロジェクトを続行します。
  • D. データの取り扱いを中止し、上司に報告して、組織および法律上のベストプラクティスに従ってデータが処理されるようにします。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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