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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Monitoring and Alerting | - Alerting
|
| Cost & Performance Optimization | - Optimize cost and performance
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Debugging and Deploying | - Debugging and Troubleshooting
|
| Data Modeling | - Design and optimize data models
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Data Sharing and Federation | - Share and federate data
|
| Data Transformation, Cleansing, and Quality | - Transform and validate data
|
| Ensuring Data Security and Compliance | - Applying Data Security Mechanisms
|
| Data Governance | - Govern enterprise data
|
Databricks Certified Data Engineer Professional Sample Questions:
1. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?
A) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
B) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing 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.
2. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?
A) Run unit tests against non-production data that closely mirrors production
B) Define units test and functions within the same notebook
C) Define and unit test functions using Files in Repos
D) Define and import unit test functions from a separate Databricks notebook
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 tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
A) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
B) All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
C) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
D) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
E) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
4. 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) Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
B) Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
C) Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
D) Periodically run OPTIMIZE table_name.
E) Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
5. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?
A) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
B) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
C) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
D) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: C,D | Question # 5 Answer: C |
