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

Databricks-Certified-Data-Engineer-Professional

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

Exam Name: Databricks Certified Data Engineer Professional Exam

Updated: Sep 12, 2026

Q & A: 250 Questions and Answers

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

SectionObjectives
Topic 1: Delta Lake and Data Management- Delta Lake transactions and ACID properties
- Time travel and versioning
- Schema evolution and enforcement
Topic 2: Production Pipelines and Orchestration- Databricks Workflows
- Job scheduling and monitoring
- Error handling and recovery strategies
Topic 3: Databricks Lakehouse Platform Architecture- Medallion architecture (Bronze, Silver, Gold)
- Workspace and cluster architecture
- Data governance concepts (Unity Catalog basics)
Topic 4: Data Modeling and Transformation- Dimensional modeling concepts
- Spark SQL transformations
- Performance optimization techniques
Topic 5: Data Ingestion and Processing- Structured Streaming fundamentals
- ETL pipeline design patterns
- Batch and streaming ingestion with Auto Loader

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A. Use Delta Lake time travel to identify and remove duplicates
B. Implement a write operation using MERGE INTO with a unique key
C. Configure Structured Streaming with dropDuplicates transformation
D. Use Delta Lake's change data feed to filter duplicate records


Question #2

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
B. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
C. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
D. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin


Question #3

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 task A fails during a scheduled run, which statement describes the results of this run?

A. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
E. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.


Question #4

Which statement describes a key benefit of an end-to-end test?

A. It makes it easier to automate your test suite
B. It closely simulates real world usage of your application.
C. It provides testing coverage for all code paths and branches.
D. It pinpoint errors in the building blocks of your application.


Question #5

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

A. CPU Utilization is around 75%
B. The five Minute Load Average remains consistent/flat
C. Network I/O never spikes
D. Bytes Received never exceeds 80 million bytes per second
E. Total Disk Space remains constant


Solutions:

Question #1
Answer: B
Question #2
Answer: D
Question #3
Answer: A
Question #4
Answer: B
Question #5
Answer: A

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