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

Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
B) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
C) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

2. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A) COPY INTO SQL command with parameters to track processed files
B) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
C) Auto Loader with schema inference and evolution enabled
D) Traditional batch processing with scheduled Databricks Jobs

3. A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A) latitude
B) User_id
C) Post_id
D) Post_time
E) Date

4. The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A) The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
B) The source query failed to update properly for three consecutive minutes and then restarted
C) The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
D) The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
E) The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

5. 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?

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(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))
)
D) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")

Solutions: