Databricks Certified Data Engineer Professional Sample Questions:

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

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

2. A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A) Enable auto-compaction (optimizeWrite and autoCompact).
B) Enable Delta caching.
C) Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
D) Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.

3. A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

A) Workspace users receive USE CATALOG and specific privileges on default schema only.
B) New users only receive CREATE TABLE privileges on the default schema.
C) Workspace catalog permissions are not subject to inheritance rules.
D) Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically

4. A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A) Use broadcast join.
B) Repartition by region before aggregation.
C) Use coalesce() after the aggregation.
D) Caching the DataFrame df.

5. A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
C) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
D) When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.

Solutions: