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 30, 2026   Q&As: 250 Questions and Answers

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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

  • A. Total VMs: 2
    200 GB per Executor
    80 Cores / Executor
  • B. Total VMs: 8
    50 GB per Executor
    20 Cores / Executor
  • C. Total VMs: 1
    400 GB per Executor
    160 Cores / Executor
  • D. Total VMs: 4
    100 GB per Executor
    40 Cores/Executor
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

  • A. Create a UDF that hardcodes allowed groups and apply it as a column mask.
  • B. Apply a column mask that references the group_access mapping table in its UDF.
  • C. Use a row filter to restrict access based on the user's group.
  • D. Create a view without selecting the sensitive column.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

  • A. Yes; Delta Lake supports infinite concurrent writers.
  • B. Yes; both of the streams can share a single checkpoint directory.
  • C. No; only one stream can write to a Delta Lake table.
  • D. No; each of the streams needs to have its own checkpoint directory.
  • E. No; Delta Lake manages streaming checkpoints in the transaction log.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

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. The five Minute Load Average remains consistent/flat
  • B. Network I/O never spikes
  • C. Total Disk Space remains constant
  • D. CPU Utilization is around 75%
  • E. Bytes Received never exceeds 80 million bytes per second
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

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. Configure Structured Streaming with dropDuplicates transformation
  • B. Implement a write operation using MERGE INTO with a unique key
  • C. Use Delta Lake's change data feed to filter duplicate records
  • D. Use Delta Lake time travel to identify and remove duplicates
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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