2026 Exam Databricks-Certified-Professional-Data-Engineer Review | Databricks Certified Professional Data Engineer Exam 100% Free Authentic Exam Questions

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

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Real Exam Qty:	59
Available Languages:	English, Portuguese (Brazil), Japanese, Korean
Passing Score:	70%
Exam Price:	USD 200
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Associate Data Engineer
Exam Format:	Multiple Choice
Certificate Validity Period:	2 years
Recommended Training:	Databricks Data Engineer Professional Training
Exam Registration:	Databricks Official Certification Registration
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored or onsite test center
Pre Condition:	No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-professional

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By earning the Databricks Certified Professional Data Engineer certification, data professionals can demonstrate their expertise in using the Databricks platform to build and manage data solutions. Databricks Certified Professional Data Engineer Exam certification can help individuals advance their careers, as well as provide organizations with a way to identify and hire qualified data professionals who can help them achieve their data-driven goals.

To prepare for the Databricks Certified Professional Data Engineer exam, candidates can take advantage of the resources provided by Databricks. The company offers training courses, certification study guides, and practice exams to help candidates prepare for the exam. These resources provide an overview of the exam topics and offer hands-on experience with the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

A. A lightweight single-node cluster with low worker node count to reduce costs.
B. An all-purpose cluster always kept running to ensure low-latency job startup times.
C. A high-concurrency cluster designed for interactive SQL workloads.
D. A job cluster configured to autoscale across multiple workers during the pipeline run.

Answer: D

Explanation:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.
Reference Source: Databricks Compute and Job Cluster Configuration Documentation - "Autoscaling and Job Clusters."

NEW QUESTION # 60
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. An error will occur because Delta Lake prevents the deletion of production data.
B. The table will be removed from the catalog and the data will be deleted.
C. Data will be marked as deleted but still recoverable with Time Travel.
D. The table will be removed from the catalog but the data will remain in storage.
E. Nothing will occur until a COMMIT command is executed.

Answer: B

Explanation:
Explanation
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table

NEW QUESTION # 61
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
B. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
C. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
D. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Answer: D

Explanation:
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 62
What are the different ways you can schedule a job in Databricks workspace?

A. Cron, On Demand runs
B. Continuous, Incremental
C. Cron, File notification from Cloud object storage
D. On-Demand runs, File notification from Cloud object storage
E. Once, Continuous

Answer: A

Explanation:
Explanation
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax

NEW QUESTION # 63
The below spark command is looking to create a summary table based customerId and the number of times the customerId is present in the event_log delta table and write a one-time micro-batch to a summary table, fill in the blanks to complete the query.
1.spark._________
2. .format("delta")
3. .table("events_log")
4. .groupBy("customerId")
5. .count()
6. ._______
7. .format("delta")
8. .outputMode("complete")
9. .option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/")
10. .trigger(______)
11. .table("target_table")

A. writeStream, readStream, once
B. writeStream, processingTime = once
C. writeStream, readStream, once = True
D. readStream, writeStream, once
E. readStream, writeStream, once = True

Answer: E

Explanation:
Explanation
The answer is readStream, writeStream, once = True.
spark.readStream
format("delta")
table("events_log")
groupBy("customerId")
count()
writeStream
format("delta")
outputMode("complete")
option("checkpointLocation", "/tmp/delta/eventsByCustomer/_checkpoints/") trigger(once = True) table("target_table")

NEW QUESTION # 64
......

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