Certified Associate Developer for Apache Spark Practice Test

Certified Associate Developer for Apache Spark Practice Test practice questions, page 13 of 14, with answer review and explanations.

Question 61

Which of the following storage levels should be used to store as much data as possible in memory on two cluster nodes while storing any data that does not fit in memory on disk to be read in when needed?

  1. MEMORY_ONLY_2
  2. MEMORY_AND_DISK_SER
  3. MEMORY_AND_DISK
  4. MEMORY_AND_DISK_2
  5. MEMORY_ONLY

Question 62

Which of the following Spark properties is used to configure the maximum size of an automatically broadcasted DataFrame when performing a join?

  1. spark.sql.broadcastTimeout
  2. spark.sql.autoBroadcastJoinThreshold
  3. spark.sql.shuffle.partitions
  4. spark.sql.inMemoryColumnarStorage.batchSize
  5. spark.sql.adaptive.skewedJoin.enabled

Question 63

Which of the following Spark properties is used to configure whether skewed partitions are automatically detected and subdivided into smaller partitions when joining two DataFrames together?

  1. spark.sql.adaptive.skewedJoin.enabled
  2. spark.sql.adaptive.coalescePartitions.enable
  3. spark.sql.adaptive.skewHints.enabled
  4. spark.sql.shuffle.partitions
  5. spark.sql.shuffle.skewHints.enabled

Question 64

Which of the following statements about the Spark DataFrame is true?

  1. Spark DataFrames are mutable unless they've been collected to the driver.
  2. A Spark DataFrame is rarely used aside from the import and export of data.
  3. Spark DataFrames cannot be distributed into partitions.
  4. A Spark DataFrame is a tabular data structure that is the most common Structured API in Spark.
  5. A Spark DataFrame is exactly the same as a data frame in Python or R.

Question 65

Which of the following operations can be used to return a new DataFrame from DataFrame storesDF without columns that are specified by name?

  1. storesDF.filter()
  2. storesDF.select()
  3. storesDF.drop()
  4. storesDF.subset()
  5. storesDF.dropColumn()