Databricks Certified-Data-Engineer-Professional

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
B. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
C. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
D. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Question 2

A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

A. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
B. Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
C. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
D. Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

Question 3

A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A. Delta APPEND_ONLY
B. Delta OPTIMIZE
C. Delta VACUUM
D. Delta Time Travel

Question 4

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Attach To" privileges on the required cluster
B. Cluster creation allowed. "Can Restart" privileges on the required cluster
C. "Can Restart" privileges on the required cluster
D. "Can Manage" privileges on the required cluster
E. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

Question 5

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
B. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
C. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
D. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour

Solutions: