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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

A. databricks run my_project_job -t prod
B. databricks bundle run my_project_job -t prod
C. databricks job run my_project_job --env prod
D. databricks execute my_project_job -e prod

Question 2

Which distribution does Databricks support for installing custom Python code packages?

A. sbt
B. jars
C. CRAM
D. nom
E. CRAN
F. Wheels

Question 3

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
B. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
C. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
D. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.

Question 4

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. functional
B. Unit
C. Integration
D. Manual
E. End-to-end

Question 5

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. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
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. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Solutions: