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

  • Exam Code: Databricks-Certified-Data-Analyst-Associate日本語
  • Exam Name: Databricks Certified Data Analyst Associate Exam (Databricks-Certified-Data-Analyst-Associate日本語版)
  • Updated: Oct 14, 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
  • 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 2
  • 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.
Topic 3
  • 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 4
  • 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 5
  • 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.

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

データエンジニアは、Databricks SQLダッシュボードを1日1回更新するようにスケジュール設定したいと考えているが、関連するSQLエンドポイントは必要な時だけ実行させたいと考えている。
データエンジニアは、ダッシュボードの更新スケジュールで使用されるSQLエンドポイントの総実行時間を最小限に抑えるために、次のうちどの方法を使用できますか?

  • A. ダッシュボードの SQL エンドポイントが、含まれているクエリの SQL エンドポイントの 1 つではないことを保証できます。
  • B. ダッシュボードの SQL エンドポイントが各クエリの SQL エンドポイントと一致するようにすることができます。
  • C. SQLエンドポイントのクラスタサイズを縮小できます。
  • D. ダッシュボードの SQL エンドポイントをサーバーレスに設定できます。
  • E. SQLエンドポイントの自動停止機能を有効にできます。
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

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Question #2

データアナリストが、NULL値がゼロのテーブルに対して複雑な集計処理を行っており、クエリの結果は以下のとおりです。
上記の結果を得るために、アナリストは次のクエリのうちどれを実行しましたか?

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

Correct Answer: B  🗳️

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Question #3

Databricks の Partner Connect とは何ですか?

  • A. 共通 API を通じて Databricks パートナー ツールを無料で使用できます。
  • B. Databricks パートナーを介してサードパーティ ツールに接続情報を公開します。
  • C. Databricks SQL Warehouse (旧称 SQL エンドポイント) で Databricks パートナー ツールを実行する機能です。
  • D. Databricks と Databricks パートナー間の多方向接続が容易になります。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

データサイエンティストが機械学習モデルのハイパーパラメータを調整したいと考えています。彼らはSpark MLモデルを目的関数objective_functionにラップし、探索空間search_spaceを定義しました。
その結果、彼らは以下のコードブロックを持っています。
num_evals = 100
トライアル = SparkTrials()
best_hyperparam = fmin(
fn=目的関数、
スペース=検索スペース、
algo=tpe.suggest、
max_evals=num_evals、
試行=試行
)
タスクを完了するために、上記のコードブロックに対して以下のどの変更を行う必要がありますか?

  • A. fmin() を fmax() に変更します
  • B. num_evalを10未満に減らす
  • C. trials=trials 引数を削除します
  • D. algo=tpe.suggest 引数を削除します
  • E. SparkTrials() を Trials() に変更します
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

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Question #5

Tableau、Power BI、Looker などの他のビジネス インテリジェンス (BI) ツールに関連して Databricks SQL を使用する方法を説明しているのは次のうちどれですか。

  • A. プラットフォーム内での迅速なBl作業のための補助ツールとして
  • B. 機能が少ない代替品として
  • C. プロフェッショナルレベルのプレゼンテーションのための補助ツールとして
  • D. 追加機能を備えた完全な代替として
  • E. 同じ機能レベルの完全な代替品として
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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