Prepare with Databricks Databricks-Certified-Data-Engineer-Associate日本語 exam practice material, pass for sure

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

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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
  • 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 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
Real Exam Qty:45
Available Languages:Korean, English, Portuguese (Brazil), Japanese
Passing Score:Not publicly disclosed, approximately 70%
Exam Price:200 USD
Exam Duration:90 minutes
Related Certifications:Databricks Certified Data Analyst Associate
Databricks Certified Data Engineer Professional
Exam Format:Proctored, Multiple choice
Certificate Validity Period:2 years
Recommended Training:Databricks Academy - Data Engineer Associate Learning Path
Databricks Documentation
Exam Registration:Official Registration
Sample Questions:Databricks Databricks-Certified-Data-Engineer-Associate日本語 Sample Questions
Exam Way:Online proctored or in-person at authorized test centers
Pre Condition:No mandatory prerequisites; 6+ months hands-on experience with Databricks and data engineering tasks recommended
Official Syllabus URL: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

データエンジニアがDatabricks Spark UIで処理が遅いステージを調査しています。エンジニアはタスク実行時間のサマリーメトリクスセクションまでスクロールダウンし、そのステージについて以下の情報を確認します。
25パーセンタイル持続時間:40秒
平均持続時間:45秒
75パーセンタイル持続時間:50秒
最大持続時間:80秒
これらの指標はどのように解釈すればよいのでしょうか?

  • A. クラスターのリソースが不足しています。
  • B. このステージはデータに偏りがある可能性が高いです。
  • C. ステージは健康です。
  • D. GC の問題が発生しています。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

データエンジニアは、分析ワークロード向けに設計されたサーバーレスコンピューティング環境を使用して、Delta Lakeに保存されているデータに対してSQLクエリを直接実行したいと考えています。この機能を提供するDatabricksコンポーネントはどれですか?

  • A. Databricks SQL Warehouse
  • B. MLflow
  • C. DBFS
  • D. スパークドライバー
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #3

Lakeflow Jobsワークフローが、ノートブックタスクのエラーにより最後のタスクで失敗しました。このワークフローは毎日実行されるようにスケジュールされています。データエンジニアはノートブックを修正し、パイプラインを再実行したいと考えています。このワークフローは計算負荷が高く、実行コストも高額です。データエンジニアは、ダウンタイムと計算コストを最小限に抑えるために、どのような対策を講じるべきでしょうか?

  • A. クラスターを再起動します
  • B. ワークフロー全体を再実行する
  • C. 別のクラスターに切り替える
  • D. 修理実行
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

データエンジニアは、COPY INTOコマンドを使用して、外部の場所からCSVファイルをDeltaテーブルにロードします。
同じジョブが同じ1時間以内に誤って2回実行されてしまった。
期待される結果は何ですか?

  • A. 2回目の実行は重複ファイルエラーで失敗し、1回目の実行がロールバックされます。
  • B. 対象テーブルは最新のファイルのみで上書きされます。
  • C. 2回目の実行では、既に取り込まれていないファイルのみがロードされます。これは、COPY INTOがロードされたファイルを冪等的に追跡するためです。
  • D. COPY INTO は常にソース パス内のすべてのファイルを再読み込みするため、レコードが重複します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).

Question #5

データエンジニアがDatabricks上でSpark SQLジョブを実行中に、小さなルックアップテーブルと大きなファクトテーブル間の結合が遅いことに気づきました。データエンジニアは、結合を高速化するために、Sparkが小さなルックアップテーブルをすべてのエグゼキュータに自動的に送信するようにしたいと考えています。データエンジニアはどの構成パラメータを調整すべきでしょうか?

  • A. spark.executor.memory
  • B. spark.sql.autoBroadcastJoinThreshold
  • C. spark.default.parallelism
  • D. spark.sql.shuffle.partitions
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).

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