Official Bank
0/215
Page
1/4
Databricks Certified Data Engineer Professional (Data Engineer Professional) - Databricks Exam Questions
Last updated on June 20, 2026
97%
Exam Compliance
215
Total Questions
1
Question
What is true for Delta Lake?
Options
A
Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B
Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C
Z-ORDER can only be applied to numeric values stored in Delta Lake tables.
D
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters.
Discussion
(0 comments)
to join the discussion
Community Discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
2
Question
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
Options
A
Configure a job that executes every time new data lands in a given directory.
B
Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
C
Schedule a job to execute the pipeline once hour on a new job cluster.
D
Schedule a Structured Streaming job with a trigger interval of 60 minutes.
Discussion
(0 comments)
to join the discussion
Community Discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
3
Question
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?
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?
Options
A
Use broadcast join.
B
Caching the DataFrame df.
C
Use coalesce() after the aggregation.
D
Repartition by region before aggregation.
Discussion
(0 comments)
to join the discussion
Community Discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
4
Question
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
Options
A
Option E
B
Option A
C
Option C
D
Option B
E
Option D
Discussion
(0 comments)
to join the discussion
Community Discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
5
Question
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States. The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed. Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
Options
A
Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
B
Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
C
Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
D
Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
E
Databricks leverages user workstations as the driver during interactive development; as such,
users should always use a workspace deployed in a region they are physically near.
users should always use a workspace deployed in a region they are physically near.
Discussion
(0 comments)
to join the discussion
Community Discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
Finish Practice?
Are you sure you want to finish? This will end your practice session.