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Certifications have become the common currency of IT careers. PrepAwayPDF's Databricks-Certified-Professional-Data-Engineer practice questions turn your Databricks Certified Professional Data Engineer ambition into a daily habit you can keep on any device — laptop, tablet, or phone.

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
Exam Duration:90 minutes
Real Exam Qty:60
Certificate Validity Period:2 years
Passing Score:70%
Available Languages:English
Exam Format:Multiple Choice, Multiple Select
Exam Price:$200 USD
Related Certifications:Databricks Certified Data Analyst Associate
Databricks Certified Associate Developer
Sample Questions: DOWNLOAD DEMO
Exam Way:Online proctored exam (Pearson VUE)
Pre Condition:Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended
Official Syllabus URL:https://www.databricks.com/learn/certification/professional-data-engineer

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

SectionWeightObjectives
Delta Lake20-25%- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Time travel and data versioning
  • 3. Optimize and Z-order
- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Delta Live Tables
  • 3. Merge, update, delete operations
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Differences between data lake, data warehouse, and lakehouse
  • 2. Bronze, silver, gold data layers
  • 3. Data governance fundamentals
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Monitoring and alerting
  • 2. Jobs and job scheduling
  • 3. Task dependencies and orchestration
Data Processing with Spark25-30%- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions
- Python and SQL for data engineering
  • 1. Built-in and user-defined functions
  • 2. Performance optimization techniques
  • 3. Spark APIs in Python
Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Spark APIs for ingestion
  • 2. DBR autoloader
  • 3. Integration with external systems

Databricks Databricks-Certified-Professional-Data-Engineer Exam — Asked and Answered

At its core, the Databricks-Certified-Professional-Data-Engineer exam is Databricks's gatekeeper for the Databricks Certification certification — a Professional-level credential. It examines how well you apply the Databricks Certified Professional Data Engineer objectives to realistic scenarios, which is why hands-on candidates tend to outperform pure memorizers. The credential also links into a wider path covering Databricks Certified Associate Developer, Databricks Certified Data Analyst Associate, so it works as both a standalone achievement and a stepping stone. In a market where certificates increasingly signal ability, passing Databricks-Certified-Professional-Data-Engineer is a concrete, verifiable upgrade to your professional profile.

The Databricks Certified Professional Data Engineer exam draws its questions from 5 domains, starting with Delta Lake (20-25%), Data Processing with Spark (25-30%), and Pipeline Development and Orchestration (10-15%). Smart candidates read weightings as a resource map: heavy domains get the deepest practice, light domains get efficient review. You'll find the complete domain list in the exam topics section above — let it drive your schedule.

The Databricks Certified Professional Data Engineer exam fits 60 questions into 90 minutes. Convert that into a pace before exam day: total minutes divided by question count gives you a per-item budget, and the discipline to flag-and-return when something overruns it. Practicing under a live timer — the PrepAwayPDF test engines run one on every session — turns that discipline into reflex, so the clock works for you instead of against you.

The Databricks Certified Professional Data Engineer exam requires 70% to pass, and Databricks charges $200 USD per attempt — full price, every time, including retakes. That pricing structure rewards preparation: run timed self-assessments with PrepAwayPDF practice questions, and only book your seat once your scores clear the bar consistently across multiple sessions. One well-prepared attempt is dramatically cheaper than two hopeful ones.

Officially, the Databricks Certified Professional Data Engineer exam uses these formats: Multiple Choice, Multiple Select. Formats shape strategy — long scenario stems reward reading the final question sentence first, while multi-select items punish partial knowledge. The fix is exposure: PrepAwayPDF's 217 practice questions rehearse you in every listed format until the mechanics are second nature and only the content demands your attention.

Databricks currently delivers the Databricks-Certified-Professional-Data-Engineer exam in English. Select whichever language lets you parse questions fastest under time pressure — borderline scores are often decided by reading speed, not knowledge. If English is your choice, daily sessions with PrepAwayPDF's English Databricks-Certified-Professional-Data-Engineer practice questions build exactly the vocabulary the exam uses.

The credential earned through the Databricks Certified Professional Data Engineer exam remains valid for 2 years. Note the expiry date the day you pass and start researching recertification options well in advance — planned renewals are routine, last-minute ones are stressful. Because recertification rules belong to Databricks and change over time, verify the current policy on the official certification page as your renewal window approaches.

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Databricks Certified Professional Data Engineer Sample Questions:

The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake ' s time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

  • A. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
  • B. Because Delta Lake ' s delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
  • C. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
  • D. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
  • E. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

  • A. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
  • B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
  • C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
  • D. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
  • E. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format( " parquet " )
.load( " /mnt/raw_orders/ " )
.withWatermark( " time " , " 2 hours " )
.dropDuplicates([ " customer_id " , " order_id " ])
.writeStream
.trigger(once=True)
.table( " orders " )
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

  • A. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
  • B. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
  • C. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
  • D. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id .
What should the data engineer do to ensure events are sequenced correctly?

  • A. O Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
  • B. O Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
  • C. O Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
  • D. O Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers , one driver of type i3.xlarge, and should use the ' 14.3.x-scala2.12 ' runtime.
Which command should the data engineer use?

  • A. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
  • B. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
  • C. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name DataEngineer_cluster
  • D. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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