• 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
  • 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 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
  • 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 4
  • 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 5
  • 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.

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:

Delta Lakeを使用して電子カルテ(EHR)を保存している医療機関において、データアナリストは、最近のデータ修正が適用される前の2週間前のpatient_recordsテーブルのスナップショットを分析する必要があります。データエンジニアは、アナリストがその特定の以前のバージョンを照会できるようにするために、どのようなアプローチを取るべきでしょうか?

  • A. VACUUMコマンドを使用して、2週間以上前のテーブルのすべてのバージョンを削除し、アナリストが残りのバージョンをクエリできるようにします。
  • B. テーブルを切り捨ててすべてのデータを削除し、その後、2週間前のデータを切り捨てたテーブルに再ロードして、アナリストがクエリを実行できるようにします。
  • C. RESTOREコマンドを使用してテーブルを2週間前のバージョンに復元し、アナリストに復元されたテーブルをクエリさせます。
  • D. Deltaトランザクションログから2週間前のバージョン番号を特定し、そのバージョン番号をアナリストと共有してVERSION AS OF構文を使用してクエリを実行するか、そのバージョンを新しいDeltaテーブルにエクスポートしてアナリストがクエリを実行します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

データエンジニアがDataFrameをDeltaテーブルに書き込んでおり、国などの特定の列に基づいてデータを物理的にディレクトリに分割したいと考えています。どのSpark DataFrameライターオプションを使用すべきでしょうか?

  • A. パーティション
  • B. 分配者
  • C. orderBy
  • D. groupBy
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

データエンジニアは、ブロンズテーブルのデータクレンジングを担当しています。要件は、customer_emailフィールドまたはcustomer_phoneフィールドのいずれかがnullである行を削除することです。このデータクレンジングは、単一のメソッド呼び出しを使用して単一の操作で実行する必要があります。複数の列のnullを1回の呼び出しでフィルタリングできるPySparkのアプローチはどれですか?

  • A. df.dropna(subset=['customer_email', 'customer_phone'])
  • B. df.na.drop(how='all')
  • C. df.filter(col('customer_email').isNotNull() & col('customer_phone').isNotNull())
  • D. df.where('customer_email IS NOT NULL').where('customer_phone IS NOT NULL')
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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データ エンジニアには複雑な実行スケジュールを持つジョブがあり、そのスケジュールを他のジョブに転送したいと考えています。
データ エンジニアは、Databricks のスケジュール フォームで各値を手動で選択するのではなく、次のどのツールを使用してプログラムでスケジュールを表し、送信できますか。

  • A. pyspark.sql.types.DateType
  • B. 日時
  • C. pyspark.sql.types.TimestampType
  • D. Cron構文
  • E. この情報をプログラムで表現して送信する方法はありません
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

開発者は、ブロンズテーブルのレコードをシルバーテーブルに処理するデータパイプラインを構築しています。ブロンズテーブル(bronze_events)には、上流のデータ取り込みシステムからの少なくとも1回の配信保証により、重複レコードが含まれています。開発者は、データを準備するために次のPySparkコードを記述します。

開発者は、引数なしで dropDuplicates() を実行した後、event_timestamp 列のみが異なる行が残っていることに気づきました。開発者は、user_id と event_type のみに基づいて重複を削除し、これら 2 つの列の一意の組み合わせごとに 1 つの行を残したいと考えています。
どのコード変更によって、この重複排除の要件が満たされますか?

  • A. df.dropDuplicates() を df.dropDuplicates(["user_id", "event_type"]) に置き換えます。
  • B. df.dropDuplicates() を df.drop("event_timestamp").dropDuplicates() に置き換えます
  • C. df.dropDuplicates() を df.groupBy("user_id", "event_type").count() に置き換えてください。
  • D. df.dropDuplicates() を df.distinct().select("user_id", "event_type") に置き換えます。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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