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Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Professional-Data-Engineer
Exam Name:
Databricks Certified Data Engineer Professional Exam
Certification:
Databricks Certification
Vendor:
Databricks
Questions:
195
Last Updated:
Jan 9, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

Databricks-Certified-Professional-Data-Engineer: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Data Engineer Professional Exam Questions and Answers

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Question 1

A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:

SELECT COUNT (*) FROM table -

Which of the following describes how results are generated each time the dashboard is updated?

Options:

A.

The total count of rows is calculated by scanning all data files

B.

The total count of rows will be returned from cached results unless REFRESH is run

C.

The total count of records is calculated from the Delta transaction logs

D.

The total count of records is calculated from the parquet file metadata

E.

The total count of records is calculated from the Hive metastore

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Question 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?

Options:

A.

Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

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 overall size of data in the table

D.

Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Question 3

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.

Which code snippet completes this function definition?

def new_records():

Options:

A.

return spark.readStream.table("bronze")

B.

return spark.readStream.load("bronze")

C.

D.

return spark.read.option("readChangeFeed", "true").table ("bronze")

E.

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