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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
Which statement describes Delta Lake optimized writes?
A. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
B. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
C. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
D. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
Question #2
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
A. databricks job run my_project_job --env prod
B. databricks bundle run my_project_job -t prod
C. databricks run my_project_job -t prod
D. databricks execute my_project_job -e prod
Question #3
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?
A. Bytes Received never exceeds 80 million bytes per second
B. Total Disk Space remains constant
C. Overall cluster CPU utilization is around 25%
D. The five Minute Load Average remains consistent/flat
E. Network I/O never spikes
Question #4
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?
A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
B. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
Question #5
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?
A. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
B. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
D. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: B | Question #3 Correct Answer: C | Question #4 Correct Answer: D | Question #5 Correct Answer: D |
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