Associate-Developer-Apache-Spark 무료 덤프문제 온라인 액세스

Which of the following is a viable way to improve Spark's performance when dealing with large amounts of data, given that there is only a single application running on the cluster?

• A.Increase values for the properties spark.dynamicAllocation.maxExecutors, spark.default.parallelism, and spark.sql.shuffle.partitions
• B.Increase values for the properties spark.sql.parallelism and spark.sql.partitions
• C.Decrease values for the properties spark.default.parallelism and spark.sql.partitions
• D.Increase values for the properties spark.default.parallelism and spark.sql.shuffle.partitions
• E.Increase values for the properties spark.sql.parallelism and spark.sql.shuffle.partitions

Which of the following is a problem with using accumulators?

• A.Only numeric values can be used in accumulators.
• B.Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
• C.Only unnamed accumulators can be inspected in the Spark UI.
• D.Accumulator values can only be read by the driver, but not by executors.
• E.Accumulators do not obey lazy evaluation.

Which of the following code blocks saves DataFrame transactionsDf in location /FileStore/transactions.csv as a CSV file and throws an error if a file already exists in the location?

• A.transactionsDf.write.format("csv").mode("error").path("/FileStore/transactions.csv")
• B.transactionsDf.write.format("csv").mode("error").save("/FileStore/transactions.csv")
• C.transactionsDf.write.save("/FileStore/transactions.csv")
• D.transactionsDf.write("csv").mode("error").save("/FileStore/transactions.csv")
• E.transactionsDf.write.format("csv").mode("ignore").path("/FileStore/transactions.csv")

Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?

• A.transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min")
• B.transactionsDf.summary("count", "mean", "stddev", "25%", "50%", "75%", "max").show()
• C.transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min").show()
• D.transactionsDf.summary().show()
• E.transactionsDf.summary()

The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items in column value. Find the error.
Code block:
transactionsDf.orderBy('value', asc_nulls_first(col('predError')))

• A.Instead of orderBy, sort should be used.
• B.Column value should be wrapped by the col() operator.
• C.Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement.
• D.Column predError should be sorted in a descending way, putting nulls last.
• E.Column predError should be sorted by desc_nulls_first() instead.