Question 1

The data engineering team has a secret scope named ‘DataOps-Prod’ that contains all secrets needed by DataOps engineers in a production workspace.

Which of the following is the minimum permission required for the DataOps engineers to use the secrets in this scope ?

Options :

A : MANAGE permission on the “DataOps-Prod” scope

B : READ permission on the “DataOps-Prod” scope

C : MANAGE permission on each secret in the “DataOps-Prod” scope

D : READ permission on each secret in the “DataOps-Prod” scope

E : Workspace Administrator role

Answer: B

Question 2

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?

Options :

A :

All data should be deleted biweekly; Delta Lake-s time travel functionality should be leveraged to maintain a history of non-PII information.

B :

Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory

C :

Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken

D :

Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level

E :

Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

Answer: E

Question 3

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code: df = spark.read.format("parquet").load(f"/mnt/source/(date)") Which code block should be used to create the date Python variable used in the above code block?

Options :

A : date = spark.conf.get("date")

B :

input_dict = input() date= input_dict["date"]

C :

import sys date = sys.argv[1]

D : date = dbutils.notebooks.getParam("date")

E :

dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")

Answer: E

Question 4

Which of the following statements best describes Delta Lake Auto Compaction?

Options :

A : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job with Z-Ordering toward a file size of 128 MB

B : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job without Z-Ordering toward a file size of 128 MB

C : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job with Z-Ordering toward a file size of 1 GB.

D : Auto Compaction occurs after a write to a table has succeeded to check if files can further be compacted; if yes, it runs an OPTIMIZE job without Z-Ordering toward a file size of 1 GB.

E : None of the above statements correctly describe Delta Lake Auto Compaction.

Answer: B

Question 5

Which of the following describes Cron syntax in Databricks Jobs ?

Options :

A : It’s an expression to represent the maximum concurrent runs of a job

B : It’s an expression to represent the retry policy of a job

C : It’s an expression to describe the email notification events (start, success, failure)

D : It’s an expression to represent the run timeout of a job

E : It’s an expression to represent complex job schedule that can be defined programmatically

Answer: E