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Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:

1. データエンジニアリングチームのメンバーが、より大規模なデータパイプラインの一部としてスケジュール設定したいという短いノートブックを提出しました。以下のコマンドは、提示されているとおりに実行した場合に論理的に正しい結果を生成するものと仮定します。

ジョブとしてスケジュールする前にノートブックから削除する必要があるコマンドはどれですか?

A) コマンド3
B) コマンド6
C) コマンド 5
D) コマンド4
E) コマンド2

2. あるデータエンジニアは、Lakeflowの宣言型パイプラインの期待値機能を使用して、入力センサーデータのデータ品質を追跡しています。センサーは定期的に範囲外の不良データを送信し、現在、それらの行に警告フラグを付けて、正常なデータと共にSilverテーブルに書き込んでいます。そこで、新たな要件が課されました。不良行は別の隔離テーブルに隔離し、Silverテーブルには含めないようにする必要があるのです。
これはシルバーテーブルの既存のコードです:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C) @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D) @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

3. 統合テストについて説明している記述はどれですか?

A) 手動介入が必要
B) アプリケーションのサブシステム間の相互作用を検証します
C) 自動テストフレームワークが必要です
D) アプリケーションのユースケースを検証する
E) アプリケーションの個々の要素の動作を検証します

4. データエンジニアがDatabricksクエリプロファイラーでクエリの実行を確認している際、「上位演算子」パネルに、処理時間(Time Spent)とメモリピーク(Memory Peak)のメトリックが高いソート演算子が表示されていることを発見しました。Spark UIにも、頻繁なデータ流出が報告されています。データエンジニアはこの問題にどのように対処すべきでしょうか？

A) ソートする前に、DataFrame を単一のパーティションに再パーティション化します。
B) メモリ使用量を削減するためにブロードキャスト参加に切り替えます。
C) ソート操作をフィルタ操作に変換します。
D) シャッフルパーティションの数を増やして、データをより適切に分散します。

5. Unity Catalog でデータエンジニアリングワークスペースが自動的に有効化され、ワー​​クスペースカタログが作成されました。新しいチームメンバーから、デフォルトスキーマではテーブルを作成できるものの、同じワークスペースカタログ内の他のスキーマのテーブルにアクセスできないという報告がありました。なぜ新しいチームメンバーは他のスキーマのテーブルにアクセスできないのでしょうか？

A) 新しいユーザーには、デフォルトのスキーマに対する CREATE TABLE 権限のみが付与されます。
B) ワークスペース ユーザーに、USE CATALOG およびデフォルトのスキーマに対する特定の権限のみが付与されます。
C) ワークスペース カタログの権限は継承ルールの対象ではありません。
D) 他のスキーマのテーブルでは、新規ユーザーには自動的に付与されない追加のBROWSE権限が必要です。

Solutions: