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Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam
Exam Code: Databricks-Certified-Data-Engineer-Professional
Exam Name: Databricks Certified Data Engineer Professional Exam
Updated: Sep 17, 2026
Q & A: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Modeling and Transformation | - Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques |
| Topic 2: Databricks Lakehouse Platform Architecture | - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture |
| Topic 3: Production Pipelines and Orchestration | - Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies |
| Topic 4: Delta Lake and Data Management | - Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning |
| Topic 5: Data Ingestion and Processing | - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals |
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?
- A. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
- B. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
- C. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
- D. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
Correct Answer: B 🗳️
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Question #2
Which statement describes the correct use of pyspark.sql.functions.broadcast?
- A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
- B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
- C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
- D. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
- E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
Correct Answer: C 🗳️
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Question #3
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. to_interval("event_time", "5 minutes").alias("time")
- B. window("event_time", "10 minutes").alias("time")
- C. lag("event_time", "10 minutes").alias("time")
- D. window("event_time", "5 minutes").alias("time")
- E. "event_time"
Correct Answer: D 🗳️
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Question #4
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?
- A. Use Repos to merge all differences and make a pull request back to the remote repository.
- B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
- C. Use repos to merge all difference and make a pull request back to the remote repository.
- D. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
- E. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
Correct Answer: D 🗳️
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Question #5
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?
- A. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
- B. Share tables WITHOUT HISTORY and enable partitioning for better query performance.
- C. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
- D. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
Correct Answer: A 🗳️
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