Optimization Quiz

What is the primary benefit of partitioning data in PySpark?

• Improved performance

• Increased data redundancy

• Simplified data structure

• Reduced data size

Which method is used to split a DataFrame into smaller, more manageable partitions based on the values in one or more columns?

• repartition()

• coalesce()

• partitionBy()

• broadcast()

True or False: Coalesce is used to increase the number of partitions in an RDD.

• True

• False

True or False: The cache() method is a shortcut for using persist with the default storage level.

• True

• False

What is the default storage level for caching in Spark?

• MEMORY_ONLY

• MEMORY_AND_DISK

• DISK_ONLY

• MEMORY_ONLY_SER

What does the MEMORY_AND_DISK storage level do if the data does not fit in memory?

• Discards the data

• Stores the data on disk

• Compresses the data

• Splits the data into smaller partitions

Which function is used to broadcast a small DataFrame to all nodes in the cluster?

• cache()

• repartition()

• broadcast()

• coalesce()

True or False: Using UDFs (User Defined Functions) instead of built-in pyspark functions in PySpark can negatively impact performance.

• True

• False

True or False: Broadcast joins are more efficient for joining large DataFrames.

• True

• False

What is the main purpose of using broadcast variables in PySpark?

• To reduce the number of partitions

• To cache the DataFrame

• To optimize data distribution across the cluster

• To perform range partitioning