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

Databricks Certified Data Engineer Professional Sample Questions:

1. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Task queueing resulting from improper thread pool assignment.
B) Spill resulting from attached volume storage being too small.
C) Credential validation errors while pulling data from an external system.
D) Network latency due to some cluster nodes being in different regions from the source data
E) Skew caused by more data being assigned to a subset of spark-partitions.

2. Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A) spark.sql.files.maxPartitionBytes
B) spark.sql.adaptive.advisoryPartitionSizeInBytes
C) spark.sql.files.openCostInBytes
D) spark.sql.adaptive.coalescePartitions.minPartitionNum
E) spark.sql.autoBroadcastJoinThreshold

3. A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A) Federation provides read-only access that reflects the current state of source systems.
B) A separate data synchronization service must be deployed.
C) Federation creates local copies that must be manually refreshed.
D) Federation implements change data capture (CDC) from all sources.

4. A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A) Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
B) Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
C) Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
D) Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin

5. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
B) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
C) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
D) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

Solutions: