You're working with a Spark application that processes sensitive dat
a. How can you ensure that persisted data remains secure even if accessed from unauthorized sources?

• A.No additional security measures are needed, as Spark handles data security
• B.Encrypt the data before persisting it and decrypt it when needed
• C.Rely on Spark's lineage tracking to prevent unauthorized access
• D.Implement custom access control mechanisms within your application

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What technique can be employed to optimize join performance by reducing data shuffle across the network?

• A.Increasing the number of partitions
• B.Using broadcast joins for smaller datasets
• C.Decreasing the memory allocated to executors
• D.Partitioning both datasets on a different key

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You need to join a Spark DataFrame with a Hive table. How can you achieve this efficiently?

• A.Use Spark SQL syntax with the JOIN clause, specifying the join type and condition
• B.Convert the Hive table to a temporary table and then perform the join with the DataFrame
• C.Implement custom logic using Spark's RDD operations to join the data
• D.Load the Hive table data into the DataFrame and then perform an in-memory join

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What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations

• A.The serialization format of data
• B.The compression codec used for shuffle files
• C.The memory allocation for executor instances

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A team is planning to use PySpark to read data from an Apache Cassandra database. Which of the following options correctly demonstrates how to load data from a Cassandra table named in the keyspace 'sales'?

• A.
• B.
• C.
• D.

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