Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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Exam Code: Databricks-Certified-Data-Engineer-Professional

Exam Name: Databricks Certified Data Engineer Professional Exam

Updated: Sep 21, 2026

Q & A: 250 Questions and Answers

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 2: Data Processing	28%	- ETL Pipelines - Spark SQL - Structured Streaming - Data Transformation
Topic 3: Databricks Lakehouse Platform	24%	- Delta Lake - Lakehouse Architecture - Data Management - Unity Catalog
Topic 4: Monitoring and Troubleshooting	16%	- Monitoring - Troubleshooting - Performance Optimization
Topic 5: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target 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.shuffle.partitions to 512, 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. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #2

A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

A. Use apply_changes without CDF and filter rows where _soft_deleted is true.
B. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
C. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
D. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Question #3

The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #4

A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A. Use compute policies to set system properties, environment variables, and Spark configuration parameters.
B. Install libraries on DBFS
C. Create init scripts on DBFS.
D. Use secrets in init scripts to store configuration data
E. Add libraries to compute policies

Reveal Solution Discussion 0

Correct Answer: A,C 🗳️

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Question #5

A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. Total Disk Space remains constant
B. Network I/O never spikes
C. The five Minute Load Average remains consistent/flat
D. Overall cluster CPU utilization is around 25%
E. Bytes Received never exceeds 80 million bytes per second

Reveal Solution Discussion 0

Correct Answer: D 🗳️

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Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

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