When dealing with iterative algorithms in Spark that require frequent access to a large dataset, which caching strategy best optimizes performance while effectively managing memory usage?

Real Exam

Cache the dataset using MEMORY_ONLY storage level, relying on Spark‘s eviction policy to manage memory._

7.9%

Avoid caching and reload the dataset from source at each iteration to guarantee data consistency.

2.6%

Use persist(StorageLevel.MEMORY_AND_DISK_SER) for the dataset to ensure it remains in memory as much as possible, spilling to disk when necessary._

77.6%

Persist the dataset in serialized form using MEMORY_ONLY_SER to reduce memory footprint at the cost of CPU overhead for serialization/deserialization.

11.8%

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When dealing with iterative algorithms in Spark that require frequent access to a large dataset, which caching strategy best optimizes performance while effectively managing memory usage?