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Databricks Certified Data Engineer Professional (Data Engineer Professional) - Databricks Exam Questions
Last updated on June 20, 2026
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215
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1
Question
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)", "valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)", "valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
Options
A
@dlt.view@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)")def trips_data_quarantine():return spark.readStream.table("ride_and_go.telemetry.trips")
B
@dlt.table(name="trips_data_quarantine")def trips_data_quarantine():return (spark.readStream.table("raw_trips_data").filter(expr(quarantine_rules)))
C
@dlt.table@dlt.expect_all_or_drop(rules)def trips_data_quarantine():return spark.readStream.table("raw_trips_data")
D
@dlt.table(partition_cols=["is_quarantined", ])@dlt.expect_all(rules)def trips_data_quarantine():return (spark.readStream.table("raw_trips_data").withColumn("is_quarantined", expr(quarantine_rules)))
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2
Question
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy. The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
Which approach would allow using DLT expectations to validate all expected records are present in this table?
Options
A
Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
B
Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values
for report key values in a DLT expectation for the report table.
for report key values in a DLT expectation for the report table.
C
Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D
Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
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3
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.
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4
Question
Which statement regarding spark configuration on the Databricks platform is true?
Options
A
When the same spar configuration property is set for an interactive to the same interactive cluster.
B
Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
C
Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
D
The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
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5
Question
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track
which data has already been processed.
Which configuration should be set to enable this?
which data has already been processed.
Which configuration should be set to enable this?
Options
A
partitionBy
B
overwriteSchema
C
checkpointLocation
D
mergeSchema
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