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Linux Foundation Certified Kubernetes Security Specialist (CKS) - Linux Foundation Exam Questions
Last updated on June 20, 2026
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215
Total Questions
1
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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2
Question
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
Which of the following likely explains these smaller file sizes?
Options
A
Z-order indices calculated on the table are preventing file compactionC Bloom filler indices calculated on the table are preventing file compaction
B
Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
C
Databricks has autotuned to a smaller target file size based on the overall size of data in the table
D
Databricks has autotuned to a smaller target file size based on the amount of data in each partition
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3
Question
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
Options
A
Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
B
No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
C
No: the change data feed only tracks inserts and updates not deleted records.
D
No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
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4
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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5
Question
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day. Which code block accomplishes this task while minimizing potential compute costs?
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day. Which code block accomplishes this task while minimizing potential compute costs?
Options
A
Option C
B
preds.write.format("delta").save("/preds/churn_preds")C) D) E)
C
preds.write.mode("append").saveAsTable("churn_preds")
D
Option D
G. Option E
G. Option E
E
Option A
F
Option B
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