Exam Code: Databricks-Certified-Data-Engineer-Professional
Exam Name: Databricks Certified Data Engineer Professional Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Databricks Lakehouse Platform24%- Lakehouse Architecture
- Delta Lake
- Data Management
- Unity Catalog
Topic 2: Monitoring and Troubleshooting16%- Performance Optimization
- Monitoring
- Troubleshooting
Topic 3: Data Processing28%- ETL Pipelines
- Spark SQL
- Data Transformation
- Structured Streaming
Topic 4: Data Quality and Governance12%- Governance
- Data Quality
- Data Lineage
Topic 5: Data Modeling and Storage20%- Data Modeling
- File Formats
- Storage Optimization

Databricks Certified Data Engineer Professional Sample Questions:

A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

  • A. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
  • B. Merge, as the job configuration looks good.
  • C. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
  • D. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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

Correct Answer: B  🗳️

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A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

  • A. Grant all users CAN MANAGE permission on all jobs to avoid access issues.
  • B. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
  • C. Use only folder-level permissions and avoid setting permissions on individual jobs.
  • D. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

  • A. Give the analytics team direct access to the production table.
  • B. Shallow clone the table for the analytics team.
  • C. Deep clone the table for the analytics team.
  • D. Create a new table for the analytics team using a CTAS statement.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

  • A. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
  • B. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
  • C. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
  • D. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
  • E. Periodically run OPTIMIZE table_name.
Reveal Solution  Discussion  0

Correct Answer: A,E  🗳️

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