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

  • 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 19, 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 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 2
  • 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 3
  • 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 4
  • 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 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

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

Question #1

以下のコマンドのうち、データベース customer360 の場所を返すのはどれですか?

  • A. USE DATABASE customer360;
  • B. DROP DATABASE customer360;
  • C. DESCRIBE DATABASE customer360;
  • D. ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' );
  • E. 場所の説明 customer360;
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

データアナリストは、以下の表 sales_table を使用して、地域ごとの製品の売上を基準にランキングを作成するよう依頼されました。
入力テーブル:
地域別製品販売
ウエストA 1880.59
東A 2045.99
イーストB 4583.23
ウエストB 3391.19
クエリの結果は次のようになります。
地域別製品ランキング
東B1
イーストA2
ウエストB 1
ウエストA2
このタスクを実行するクエリはどれですか?

  • A. オプションB
  • B. SELECT region, product, RANK() OVER ( ORDER BY sales DESC ) AS rankFROM sales_table;
  • C. オプションC
  • D. SELECT region, product, RANK() OVER ( PARTITION BY region ) AS rankFROM sales_table;
  • E. オプションA
  • F. オプションD
  • G. SELECT region, product, RANK() OVER ( PARTITION BY region ORDER BY sales DESC ) AS rankFROM sales_table;
  • H. SELECT region, product, RANK() OVER ( PARTITION BY product ORDER BY sales DESC ) AS rankFROM sales_table;
Reveal Solution  Discussion  0

Correct Answer: G  🗳️

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Question #3

データエンジニアは、2つのテーブルからデータを取得してリレーショナルオブジェクトを作成したいと考えています。このリレーショナルオブジェクトは、他のセッションで他のデータエンジニアが使用する必要はありません。ストレージコストを削減するため、データエンジニアは物理データのコピーと保存を避けたいと考えています。
データエンジニアは、次のうちどのリレーショナルオブジェクトを作成すべきでしょうか?

  • A. デルタ表
  • B. 表示
  • C. 一時的な表示
  • D. データベース
  • E. Spark SQLテーブル
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

ステークホルダーからデータアナリストに、50行のCSVファイル形式のルックアップデータセットが提供されました。データアナリストは、このデータセットをアップロードしてDatabricks SQLのテーブルとして使用する必要があります。
データ アナリストは、Databricks SOL で使用するためにファイルをテーブルにすばやくアップロードするには、どのアプローチを使用する必要がありますか?

  • A. ファイルをクラウド ストレージにアップロードし、データを Databricks にインポートしてテーブルを作成します。
  • B. データ値を手動でコピーしてクラウド ストレージに貼り付け、そのデータを Databricks にインポートしてテーブルを作成します。
  • C. Databricks SQL 内の [作成] ページを使用してファイルをアップロードしてテーブルを作成します。
  • D. Partner Connect によって実現される Databricks とデスクトップ間の接続を介してテーブルを作成します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #5

データアナリストが管理対象テーブル「my_table」を作成し、その所有者となっています。現在、データエクスプローラーを使用して、このテーブルの所有権を他のユーザー1人に変更したいと考えています。
アナリストがタスクを完了するために使用できるアプローチは次のどれですか?

  • A. テーブルページの所有者フィールドを編集して、すべてのアクセス権を削除します。
  • B. テーブルページの所有者フィールドを編集するには、すべてのユーザーを選択します。
  • C. テーブルページの所有者フィールドを編集して、自分のアカウントを削除します。
  • D. 新しい所有者のアカウントを選択して、テーブルページの所有者フィールドを編集します。
  • E. テーブルページの所有者フィールドを編集するには、管理者グループを選択します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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