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Linux Foundation Certified Kubernetes Security Specialist (CKS) - Linux Foundation Exam Questions
Last updated on June 20, 2026
97%
Exam Compliance
215
Total Questions
1
Question
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
Options
A
Can Run
B
Can Edit
C
Can Manage
D
No permissions
E
Can Read
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2
Question
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?
Options
A
No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B
‘’Read’’ permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C
“Read” permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D
“Manage” permission should be set on a secret scope containing only those credentials that will be used by a given team.
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3
Question
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
Options
A
Deep clone the table for the analytics team.
B
Create a new table for the analytics team using a CTAS statement.
C
Give the analytics team direct access to the production table.
D
Shallow clone the table for the analytics team.
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4
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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5
Question
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?
Which operation lacks support for cluster on write?
Options
A
CTAS and RTAS statements
B
spark.writestream.format('delta').mode('append')
C
INSERT INTO operations
D
spark.write.format('delta').mode('append')
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