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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:
Apr 30, 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

Given the following PySpark code snippet in a Databricks notebook:

filtered_df = spark.read.format( " delta " ).load( " /mnt/data/large_table " ) \

.filter( " event_date > ' 2024-01-01 ' " )

filtered_df.count()

The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.

What is the probable reason for poor data skipping?

Options:

A.

The Delta table lacks optimization that enables dynamic file pruning.

B.

The filter is executed only after the full data scan, preventing data skipping.

C.

The event_date column is outside the table’s partitioning and Z-ordering scheme.

D.

The filter condition involves a data type excluded from data skipping support.

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Question 2

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM ' s resources?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

Question 3

A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

Options:

A.

Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

B.

Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

C.

Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.

D.

Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

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