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Databricks Databricks-Certified-Data-Engineer-Associate日本語 Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Data Engineer Associate Exam
Exam Number:DE-A
Available Languages:English
Real Exam Qty:45 scored multiple-choice questions
Exam Duration:90 minutes
Exam Format:Multiple-choice, Scenario-based questions, Proctored online or test center
Passing Score:Approximately ~70% (not officially fixed/publicly guaranteed)
Related Certifications:Databricks Certified Data Engineer Professional
Exam Price:USD 200
Certificate Validity Period:2 years
Recommended Training:Databricks Data Engineering Learning Path (Databricks Academy)
Official Data Engineering with Databricks Course
Exam Registration:Databricks Certification Portal
Sample Questions:Databricks Databricks-Certified-Data-Engineer-Associate日本語 Sample Questions
Exam Way:Online proctored exam or test center
Pre Condition:No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:https://www.databricks.com/learn/certification/data-engineer-associate

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

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

Reference: https://www.databricks.com/learn/certification/data-engineer-associate

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、既存の顧客情報を更新し、新規顧客情報を挿入し、重複データが作成されないように、顧客レコードのバッチを毎日デルタテーブルに適用する必要があります。
単一の原子操作でこれを実現するステートメントはどれですか?

  • A. CREATE OR REPLACE TABLE customers AS SELECT * FROM updates
  • B. INSERT OVERWRITE TABLE customers SELECT * FROM updates
  • C. INSERT INTO customers SELECT * FROM updates
  • D. MERGE INTO customers t USING updates s ON t.customer_id = s.customer_id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

Lakeflow Declarative Pipeline (Delta Live Tables) では、パイプラインの実行中に order_id が null のレコードが silver テーブルから破棄されるように、データ エンジニアが確認する必要があります。
どの期待条項を使用すべきでしょうか?

  • A. 制約 valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
  • B. 制約 valid_id は (order_id が NULL ではない) を期待します
  • C. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)
  • D. 制約 valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

データエンジニアは、eコマース取引のDeltaテーブルのデータレイアウトとクエリパフォーマンスを最適化する必要があります。このテーブルは、時間ベースのクエリに役立つ日付列「purchase_date」と、カーディナリティの高いユーザー統計列「customer_id」による検索を最適化しない日付列「purchase_date」でパーティション分割されています。
このテーブルは通常、特定の日付範囲内の「customer_id」をフィルターとしてクエリされますが、このデータは各パーティション内の複数のファイルに分散しているため、パーティション全体のスキャンが発生し、実行時間とコストが増加します。
データエンジニアは、効率的な読み取りのためにデータレイアウトをどのように最適化すべきでしょうか?

  • A. 「customer_id」と「purchase_date」に基づいて、Liquidクラスタリングを実装するテーブルを変更します。
  • B. テーブルを「customer_id」でパーティション分割するように変更します。
  • C. 既存のパーティショニングを維持しながら、「customer_id」に対してリキッドクラスタリングを実装するテーブルを変更します。
  • D. クラスター上でデルタキャッシュを有効にして、頻繁に行われる読み取りをキャッシュし、パフォーマンスを向上させます。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

データエンジニアが外部テーブルを管理対象テーブルに変換します。このテーブルからデータを読み取る構造化ストリーミングジョブは、変換中も実行され続けます。変換が完了すると、ストリーミングジョブは新しいレコードの処理を停止します。データエンジニアはこの問題をどのように解決すべきでしょうか?

  • A. 変換されたテーブルに対してREFRESH TABLEを実行し、ストリーミングチェックポイントを更新します。
  • B. 新しい管理ストレージの場所に対してストリーミングジョブに追加の権限を付与します。
  • C. ストリーミングチェックポイントディレクトリを削除し、ジョブを最初から再処理します。
  • D. ストリーミングジョブを再起動して、新しい管理対象テーブルの場所を認識させます。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).

Question #5

データエンジニアリングチームには2つのテーブルがあります。1つ目のテーブル「march_transactions」は、3月のすべての小売取引のコレクションです。2つ目のテーブル「april_transactions」は、4月のすべての小売取引のコレクションです。これらのテーブル間に重複レコードはありません。
march_transactions と april_transactions のすべてのレコードを重複レコードなしで含む新しいテーブル all_transactions を作成するには、次のコマンドのどれを実行する必要がありますか?

  • A. all_transactions テーブルを作成する
    march_transactions から * を選択
    INTERSECT SELECT * from april_transactions;
  • B. all_transactions テーブルを作成する
    march_transactions から * を選択
    外部結合 SELECT * FROM april_transactions;
  • C. all_transactions テーブルを作成する
    march_transactions から * を選択
    UNION SELECT * FROM april_transactions;
  • D. all_transactions テーブルを CREATE する
    march_transactions から * を選択
    内部結合 SELECT * FROM april_transactions;
  • E. すべてのトランザクションをテーブルとして作成する
    march_transactions から * を選択
    MERGE SELECT * FROM april_transactions;
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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