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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:Databricks-Certified-Data-Engineer-Associate
Related Certifications:Databricks Certified Data Analyst Associate
Real Exam Qty:60
Exam Price:$200 USD
Available Languages:English
Exam Duration:90 minutes
Certificate Validity Period:2 years
Exam Format:Multiple Select, Multiple Choice
Passing Score:70%
Sample Questions:Databricks Databricks-Certified-Data-Engineer-Associate日本語 Sample Questions
Exam Way:Online proctored or in-person testing center
Pre Condition:Recommended: 6+ months of experience with Databricks and Apache Spark
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
  • 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
  • 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 4
  • 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 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

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、更新と削除操作を何度か繰り返した後、参照されなくなった古いデータファイルを含むDeltaテーブルを持っています。これらの未使用ファイルを削除してストレージ領域を解放するには、どのDelta Lakeコマンドを実行すればよいでしょうか?

  • A. 歴史を説明する
  • B. 最適化
  • C. 真空
  • D. キャッシュテーブル
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #2

データエンジニアが、Databricksでバッチ処理ジョブを設定し、クラウドストレージから大規模なParquetデータセットを読み込むようにしています。エンジニアは、タスクを手動で割り当てることなく、Sparkが複数のワーカーノードに計算を自動的に分散することを望んでいます。自動並列処理を可能にするSparkの抽象化はどれですか?

  • A. クラスター
  • B. 実行者
  • C. ドライバー
  • D. データフレーム
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

データエンジニアが、PySparkを使用してデータセットを処理するDatabricks上のPythonノートブックをデバッグしています。ノートブックは、DataFrame変換中にエラーが発生して失敗します。エンジニアは、入力DataFrameや中間結果などの変数の状態を調べて、エラーが発生した箇所を特定したいと考えています。ノートブックをデバッグし、DataFrameなどの変数の値を検査するには、エンジニアはどのツールを使用すべきでしょうか?

  • A. Ganglia UIを使用してクラスタのリソース使用状況を監視し、ハードウェアの問題を特定します。
  • B. Databricks CLIを使用してドライバログをダウンロードし、詳細なエラーメッセージを分析します。
  • C. Python Notebookインタラクティブデバッガーを使用してブレークポイントを設定し、変数の値をリアルタイムで検査します。
  • D. Spark UIを使用して実行プランを分析し、ジョブが失敗したステージを特定します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

ブロンズ テーブルと生データの関係を説明するのは次のどれですか。

  • A. ブロンズ テーブルには、スキーマが適用された生データが含まれています。
  • B. ブロンズ テーブルには集計が含まれますが、生のデータは集計されません。
  • C. ブロンズ テーブルには、生データ ファイルよりも少ないデータが含まれています。
  • D. ブロンズ テーブルには、生データよりも真実に近いデータが含まれています。
  • E. ブロンズ テーブルには、生データよりも精度の低いデータ ビューが含まれています。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #5

ある組織がデータレイクハウスを構築しており、複数のソースからUnityカタログで管理されるテーブルにデータを取り込む必要がある。
ソース1:Salesforce(50以上のオブジェクト、頻繁なスキーマ変更、OAuth)
認証が必要です)
ソース 2: オンプレミス SQL サーバー データベース (100 以上のテーブル、CDC 有効、
ネットワーク接続が必要です)
ソース3:Azure Data Lake Storage Gen2に毎日JSONファイルが到着する
組織は、取り込まれるすべてのデータをUnityカタログで管理し、スキーマ変更のためのデータエンジニアリング作業を最小限に抑え、運用上のオーバーヘッドを削減するために可能な限りサーバーレスを使用することを望んでいます。
これらの要件を満たす取り込み戦略はどれですか?

  • A. Lakeflow Connectのマネージドコネクタを3つのデータソースすべてに使用してください。これにより、すべてのデータソースタイプにわたって一貫したサーバーレス実行とUnity Catalogとの統合が実現します。
  • B. カスタムPythonコードを使用してSalesforce API呼び出しとSQLサーバーCDC抽出を処理し、Unityカタログテーブルに書き込む3つのソースすべてにAuto Loaderを使用します。
  • C. Partner Connect を介して、3 つのソースすべてにパートナー コネクタ (Fivetran) を使用してください。これにより、Unity Catalog との最も包括的な統合と、スキーマの自動進化が実現されます。
  • D. Lakeflow Spark Declarative Pipelines 内で、Salesforce および SQL Server 用の Lakeflow Connect マネージド コネクタと ADLS ファイル用の Auto Loader を使用します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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