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

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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、スキーマ「main marketing」の日常的な権限管理をmkt-adminsグループに委任したいと考えているが、彼らをワークスペース管理者にはしたくない。彼らは、そのスキーマ内のオブジェクトに対して、他のユーザーへの権限付与と権限取り消しを行えるようにする必要がある。
どちらのアプローチがUCの所有権と特権モデルに合致するか?

  • A. mkt-admins に main.marketing に対する USE SCHEMA 権限と、すべてのテーブルに対する MODIFY 権限を付与します。これにより、スキーマ内での権限管理が可能になります。
  • B. mkt-admins をワークスペースレベルの管理者グループにし、次に main.marketing に対して SELECT を実行して権限委譲を許可します。
  • C. スキーマ main.marketing の所有権を mkt-admins に譲渡します。所有者は、スキーマおよびそれに含まれるオブジェクトに対する権限を管理できます。
  • D. mkt-admins にメタストアに対する MANAGE 権限を付与します。これにより、すべてのスキーマとテーブルに対するグローバルな管理権限が付与されます。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

データエンジニアが、キーと値の偏りがある非常に大きなデータセットを読み込んでいます。一部のパーティションには他のパーティションよりもレコード数が大幅に多く含まれており、エグゼキュータ間でワークロードの分散が不均一になっています。このシナリオは、Sparkのどの問題を説明していますか?

  • A. データの偏り
  • B. 遅延評価
  • C. スキーマの進化
  • D. 放送失敗
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #3

データエンジニアは、ETLプロセスの統合テストを開発し、バージョン管理されたパッケージ化されたワークフローを外部ジョブスケジューラを使用して本番環境にデプロイする必要があります。この作業には、データエンジニアはどのツールを使用すべきでしょうか?

  • A. Databricksコマンドラインインターフェース
  • B. Databricksアセットバンドル
  • C. Databricks Connect
  • D. Databricksソフトウェア開発キット
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

データエンジニアが、同じコマンドセル内でPythonとSQLを記述しようとしてエラーに遭遇しています。エンジニアは、SELECT文でPythonの変数を使用できると考えていました。なぜコマンドが失敗するのでしょうか?

  • A. Databricks は同一セル内での言語相互運用性をサポートしていますが、Scala と SQL の間のみです。
  • B. Databricksはセルごとに1つの言語をサポートしています。
  • C. Databricksは複数の言語をサポートしていますが、ノートブックごとに1つの言語のみです。
  • D. Databricksは言語の相互運用性をサポートしていますが、特殊文字が使用されている場合に限ります。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

グローバル小売企業は、複数のカテゴリー(例:電子機器、衣料品)と地域(例:北、南、東、西)にわたる製品を販売しています。営業チームは、データエンジニアに以下のような sales_df という名前の PySpark データフレームを提供し、戦略的な意思決定に役立てるために、データエンジニアに販売データの分析を依頼しています。

各製品カテゴリの総売上高を計算し、その結果をcategory_salesという名前の新しいデータフレームに格納します。
category_sales の期待される結果を生成するものは何ですか?

  • A. category_sales =
    sales_df.groupBy("category").agg(sum("sales_amount").alias("total_sales_amount"))
  • B. category_sales =
    sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
  • C. category_sales =
    sales_df.agg(sum("sales_amount").groupBy("category").alias("total_sales_amount"))
  • D. category_sales =
    sales_df.sum("sales_amount").groupBy("category").alias("total_sales_amount"))
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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