Databricks Databricks-Certified-Data-Analyst-Associate-JPN Questions & Answers - in .pdf
- Total Q&A: 118
- Update: Oct 17, 2026
- Price: $69.99
- Vendor: Databricks
- 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)
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Databricks Databricks-Certified-Data-Analyst-Associate-JPN Q&A - Testing Engine
- Total Q&A: 118
- Update: Oct 17, 2026
- Price: $69.99
- Vendor: Databricks
- 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)
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Databricks Databricks-Certified-Data-Analyst-Associate日本語 Exam Syllabus Topics:
| Topic | Details |
|---|
| 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
データ アナリストは各地域の顧客数をカウントするように依頼され、次のクエリを作成しました。
クエリに間違いがある場合、その間違いを説明するのは次のどれですか。
A. クエリは count(') を使用しています。これにより、地域に関係なく、顧客テーブル内のすべての顧客がカウントされます。
B. クエリに GROUP BY region 句がありません。
C. クエリは ORDER BY を使用しています。これは集計では許可されていません。
D. クエリに間違いはありません。
E. クエリは地域を選択していますが、地域は ORDER BY 句でのみ出現する必要があります。
Question #2
データアナリストが、テーブル「transactions」内のネストされた配列列「products」を扱っています。アナリストは、各行について配列の最初の要素を返したいと考えています。
データアナリストは、以下の不完全なコマンドを使用しています。
選択
トランザクションID、
_____ AS 最初の製品
トランザクションから;
データアナリストは、タスクを正常に完了させるために、どのコード行を使用して空欄を埋めるべきでしょうか？
A. 製品[1]
B. 製品1
C. 製品.0
D. 製品[0]
Question #3
クラウドベースのオブジェクト ストレージから直接データを取り込むために使用できるアプローチは次のどれですか?
A. オブジェクトストレージパスをLOCATIONに指定して外部テーブルを作成します。
B. DBFSストレージパスをFROMに指定して外部テーブルを作成します。
C. クラウドベースのオブジェクトストレージからデータを直接取り込むことはできません
D. FROMにオブジェクトストレージパスを指定して外部テーブルを作成します。
E. DBFSストレージパスをPATHに指定して外部テーブルを作成します。
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. 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。
B. 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。
C. 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。
D. 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。
Question #5
データ アナリストは次の出力を望んでいます。
顧客名 注文数
ジョン・ドゥ 388
張三 234
この出力を生成するステートメントはどれですか?
A. SELECT customerjiame, count(order_id)
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id GROUP BY customerjiame;
B. SELECT customerjiame, (order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id;
C. SELECT customer_name, count(order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id USE customer_name;
D. SELECT customer_name, count(order_id) AS number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id
GROUP BY customer_name;
Solutions:
Question #1
Correct Answer: B | Question #2
Correct Answer: D | Question #3
Correct Answer: A | Question #4
Correct Answer: B | Question #5
Correct Answer: D |
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