Databricks Databricks-Certified-Data-Engineer-Professional 問題集

弊社は失敗したら全額で返金することを承諾します

我々は弊社のDatabricks-Certified-Data-Engineer-Professional問題集に自信を持っていますから、試験に失敗したら返金する承諾をします。我々のDatabricks Databricks-Certified-Data-Engineer-Professionalを利用して君は試験に合格できると信じています。もし試験に失敗したら、我々は君の支払ったお金を君に全額で返して、君の試験の失敗する経済損失を減少します。

TopExamは君にDatabricks-Certified-Data-Engineer-Professionalの問題集を提供して、あなたの試験への復習にヘルプを提供して、君に難しい専門知識を楽に勉強させます。TopExamは君の試験への合格を期待しています。

弊社のDatabricks Databricks-Certified-Data-Engineer-Professionalを利用すれば試験に合格できます

弊社のDatabricks Databricks-Certified-Data-Engineer-Professionalは専門家たちが長年の経験を通して最新のシラバスに従って研究し出した勉強資料です。弊社はDatabricks-Certified-Data-Engineer-Professional問題集の質問と答えが間違いないのを保証いたします。

この問題集は過去のデータから分析して作成されて、カバー率が高くて、受験者としてのあなたを助けて時間とお金を節約して試験に合格する通過率を高めます。我々の問題集は的中率が高くて、100％の合格率を保証します。我々の高質量のDatabricks Databricks-Certified-Data-Engineer-Professionalを利用すれば、君は一回で試験に合格できます。

弊社は無料Databricks Databricks-Certified-Data-Engineer-Professionalサンプルを提供します

お客様は問題集を購入する時、問題集の質量を心配するかもしれませんが、我々はこのことを解決するために、お客様に無料Databricks-Certified-Data-Engineer-Professionalサンプルを提供いたします。そうすると、お客様は購入する前にサンプルをダウンロードしてやってみることができます。君はこのDatabricks-Certified-Data-Engineer-Professional問題集は自分に適するかどうか判断して購入を決めることができます。

Databricks-Certified-Data-Engineer-Professional試験ツール：あなたの訓練に便利をもたらすために、あなたは自分のペースによって複数のパソコンで設置できます。

一年間の無料更新サービスを提供します

君が弊社のDatabricks Databricks-Certified-Data-Engineer-Professionalをご購入になってから、我々の承諾する一年間の更新サービスが無料で得られています。弊社の専門家たちは毎日更新状態を検査していますから、この一年間、更新されたら、弊社は更新されたDatabricks Databricks-Certified-Data-Engineer-Professionalをお客様のメールアドレスにお送りいたします。だから、お客様はいつもタイムリーに更新の通知を受けることができます。我々は購入した一年間でお客様がずっと最新版のDatabricks Databricks-Certified-Data-Engineer-Professionalを持っていることを保証します。

安全的な支払方式を利用しています

Credit Cardは今まで全世界の一番安全の支払方式です。少数の手続きの費用かかる必要がありますとはいえ、保障があります。お客様の利益を保障するために、弊社のDatabricks-Certified-Data-Engineer-Professional問題集は全部Credit Cardで支払われることができます。

領収書について：社名入りの領収書が必要な場合、メールで社名に記入していただき送信してください。弊社はPDF版の領収書を提供いたします。

Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from part- file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A) Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
B) Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
C) Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
D) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
E) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

2. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

3. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
B) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
C) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

4. Which statement describes the default execution mode for Databricks Auto Loader?

A) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
B) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
C) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
D) Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
E) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

5. The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A) df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
B) model.predict(df, columns)
C) df.select("customer_id", model(*columns).alias("predictions"))
D) df.apply(model, columns).select("customer_id, predictions")
E) df.map(lambda x:model(x[columns])).select("customer_id, predictions")

質問と回答：