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Exam Code: Databricks-Certified-Data-Engineer-Associate
Exam Name: Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版)
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.
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
  • 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.

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:

次のコマンドのうち、member_id 列の null 値の数を返すものはどれですか。

  • A. my_table から count(member_id) を選択します。
  • B. my_table から count(member_id) - count_null(member_id) を選択します。
  • C. my_table から null(member_id) を選択します。
  • D. count_null(member_id) を my_table から選択します。
  • E. count_if(member_id IS NULL) を my_table から選択します。
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

データエンジニアが非常に大規模なデータセットを扱っており、関連データを同じファイルにまとめてクエリのパフォーマンスを向上させたいと考えています。エンジニアは、頻繁にフィルタリングされる列にZオーダーを適用することにしました。どのDelta Lakeコマンドを使用しますか？

  • A. 分析表
  • B. ZORDER の最適化
  • C. 真空ゾーダー
  • D. キャッシュテーブル
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

ある企業が、Databricksを使用していないものの、Delta形式で保存された大規模な履歴データセットへのアクセスを必要とするパートナーと共同作業を行っています。データエンジニアは、パートナーがアカウントを作成することなく、読み取り専用アクセスで安全にデータにアクセスできるようにする必要があります。データはどのように共有すべきでしょうか？

  • A. パートナーにDatabricksワークスペースへのアクセス権を付与し、Deltaテーブルへの完全な書き込み権限を割り当てて、データセットを変更できるようにします。
  • B. データセットをCSVファイルにエクスポートし、手動でパートナーのシステムに転送して共有します。
  • C. Delta Sharingを使用してデータセットを共有します。これにより、パートナーはDatabricksアカウントを必要とせずに、安全な読み取り専用URLを使用してデータにアクセスでき、データが変更されないことが保証されます。
  • D. Unity Catalogを使用してデータセットを共有し、両チームが同じ組織内のデータに対して完全な書き込みアクセス権を持つようにします。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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あるプロジェクトに新しいデータエンジニアリングチームが配属されました。チームは、既存のテーブルを確認するためにデータベースの顧客にアクセスする必要があります。チームには独自のグループチームがあります。
新しいチームにデータベース全体に対する必要な権限を付与するには、次のコマンドのどれを使用できますか?

  • A. カタログ チームの使用権限を顧客に付与します。
  • B. データベース 顧客に対する CREATE 権限を team に付与します。
  • C. データベース team に対する CREATE 権限を customer に付与します。
  • D. カタログ顧客のビューをチームに付与します。
  • E. データベース customers の使用権限を team に付与します。
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

データエンジニアがデータパイプラインを設計しています。ソースシステムは、他のプロセスでも使用される共有ディレクトリにファイルを生成します。そのため、ファイルはそのまま保持され、ディレクトリ内に蓄積されます。データエンジニアは、パイプラインの前回の実行以降に追加されたファイルを特定し、各実行でそれらの新しいファイルのみを取り込むようにパイプラインを設定する必要があります。
データ エンジニアがこの問題を解決するために使用できるツールは次のどれですか。

  • A. Unity カタログ
  • B. データエクスプローラー
  • C. オートローダー
  • D. データブリックスSQL
  • E. デルタ湖
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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