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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
B. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
C. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
D. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

Question 2

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
B. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
C. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
D. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
E. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

Question 3

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can run
B. Can edit
C. Can manage
D. Can Read

Question 4

A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A. It ignores deletions if they arrive after updates for the same key.
B. It manually sorts incoming events by timestamp before applying changes.
C. It runs VACUUM on the target table to purge conflicting records.
D. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.

Question 5

A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
B. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
C. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
D. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.

Solutions: