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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
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1
Question
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.
Which command should the data engineer implement?
Which command should the data engineer implement?
Options
A
Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
B
Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
C
Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
D
Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
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2
Question
A data team is implementing an append-only Delta Lake pipeline that needs to handle both batch and streaming data. They want to ensure that schema changes in the source data can be automatically incorporated without breaking the pipeline. Which configuration should the team use
when writing data to the Delta table?
when writing data to the Delta table?
Options
A
ignoreChanges = false
B
validateSchema = false
C
overwriteSchema = true
D
mergeSchema = true
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3
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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4
Question
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table. Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
Options
A
Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B
Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
C
Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
D
Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
E
Both commands will fail. No new variables, tables, or views will be created.
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5
Question
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE. The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.
Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?
Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?
Options
A
Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
B
Model, predict (df, columns)
C
Df. Select (''customer_id''.Model (''columns) alias (''predictions'')
D
Df.apply(model, columns). Select (''customer_id, prediction''
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