Databricks Certified Data Engineer Professionalは専門性の高い試験として知られ、十分な準備なしに臨むには不安が残ります。JPNTestのCertified-Data-Engineer-Professional練習問題は本試験の出題範囲に沿った250の問題で構成されており、2026年の試験対策にも対応しています。

Databricks Certified-Data-Engineer-Professional 試験概要:

Databricks Certified-Data-Engineer-Professional 試験シラバストピック:

Databricks Certified Data Engineer ProfessionalのQ&A

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

問題 #1

The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. Whenever a database is being created, make sure that the location keyword is used
C. Whenever a table is being created, make sure that the location keyword is used.
D. When tables are created, make sure that the external keyword is used in the create table statement.
E. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

問題 #2

A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A.

B.

C.

D.

問題 #3

Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
E. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

問題 #4

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
B. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
C. Instruct the marketing team to download results as a CSV and email them to the sales organization.
D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

問題 #5

A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. mergeSchema
B. overwriteSchema
C. checkpointLocation
D. partitionBy

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