When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

• A.Caching datasets that are frequently accessed and modified.
• B.Caching large datasets that do not fit into memory when stored in deserialized form.
• C.Caching small, static datasets used in lookup operations.
• D.Caching datasets that require fast, sequential access without the need for serialization.

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Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?

• A.Increase the number of Spark executors without further optimization
• B.Use a different file format for the Hive table, like CSV, for faster parsing
• C.Leverage partition pruning to only read relevant data from the table
• D.Implement custom data compression logic within Spark for improved storage efficiency

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In the context of Spark SQL, what does the Catalyst optimizer use to optimize queries?

• A.A cost-based optimization model that considers the size of intermediate data
• B.A rule-based optimization model that applies predefined rules to simplify queries
• C.Machine learning algorithms to predict the fastest query execution plan

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In the context of Cloudera's Optimization Framework, what is the purpose of dynamic partition pruning?

• A.To increase the size of partitions dynamically based on data volume
• B.To update partition metadata in real-time
• C.To dynamically eliminate unnecessary partitions from a query plan based on runtime statistics
• D.To partition data dynamically based on query execution plans

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Which of the following is true about persisting RDDs in Apache Spark?
A Persisting an RDD in memory allows for faster access but increases the risk of data loss.

• A.Persisting an RDD to disk is generally recommended for all RDDs to prevent data loss.
• B.Using MEMORY_ONLY_SER storage level reduces memory usage but increases CPU usage due to serialization.
• C.It is not possible to persist an RDD using a combination of memory and disk.

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