- 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:
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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。
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
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')
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
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。
Correct Answer: A 🗳️
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