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NEW QUESTION 22
Which is the highest level in Spark's execution hierarchy?

  • A. Stage
  • B. Executor
  • C. Job
  • D. Slot
  • E. Task

Answer: C

NEW QUESTION 23
The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId. Find the error.
Code block:
transactionsDf.write.partitionOn("storeId").parquet(filePath)

  • A. The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.
  • B. The partitionOn method should be called before the write method.
  • C. No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.
  • D. The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.
  • E. Column storeId should be wrapped in a col() operator.

Answer: C

Explanation:
Explanation
No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.
Correct! Find out more about partitionBy() in the documentation (linked below).
The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.
No. There is no information about whether files should be overwritten in the question.
The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.
Incorrect. To write a DataFrame to disk, you need to work with a DataFrameWriter object which you get access to through the DataFrame.writer property - no parentheses involved.
Column storeId should be wrapped in a col() operator.
No, this is not necessary - the problem is in the partitionOn command (see above).
The partitionOn method should be called before the write method.
Wrong. First of all partitionOn is not a valid method of DataFrame. However, even assuming partitionOn would be replaced by partitionBy (which is a valid method), this method is a method of DataFrameWriter and not of DataFrame. So, you would always have to first call DataFrame.write to get access to the DataFrameWriter object and afterwards call partitionBy.
More info: pyspark.sql.DataFrameWriter.partitionBy - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

NEW QUESTION 24
Which of the following describes the characteristics of accumulators?

  • A. Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.
  • B. Accumulators are immutable.
  • C. All accumulators used in a Spark application are listed in the Spark UI.
  • D. Accumulators are used to pass around lookup tables across the cluster.
  • E. If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.

Answer: E

Explanation:
Explanation
If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.
Correct, when Spark tries to rerun a failed action that includes an accumulator, it will only update the accumulator if the action succeeded.
Accumulators are immutable.
No. Although accumulators behave like write-only variables towards the executors and can only be read by the driver, they are not immutable.
All accumulators used in a Spark application are listed in the Spark UI.
Incorrect. For scala, only named, but not unnamed, accumulators are listed in the Spark UI. For pySpark, no accumulators are listed in the Spark UI - this feature is not yet implemented.
Accumulators are used to pass around lookup tables across the cluster.
Wrong - this is what broadcast variables do.
Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.
Wrong, accumulators are instantiated via the accumulator(n) method of the sparkContext, for example: counter
= spark.sparkContext.accumulator(0).
More info: python - In Spark, RDDs are immutable, then how Accumulators are implemented? - Stack Overflow, apache spark - When are accumulators truly reliable? - Stack Overflow, Spark - The Definitive Guide, Chapter 14

NEW QUESTION 25
Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?

  • A. transactionsDf.groupBy("productId").count()
  • B. transactionsDf.count("productId").distinct()
  • C. transactionsDf.groupBy("productId").agg(col("value").count())
  • D. transactionsDf.count("productId")
  • E. transactionsDf.groupBy("productId").select(count("value"))

Answer: A

Explanation:
Explanation
transactionsDf.groupBy("productId").count()
Correct. This code block first groups DataFrame transactionsDf by column productId and then counts the rows in each group.
transactionsDf.groupBy("productId").select(count("value"))
Incorrect. You cannot call select on a GroupedData object (the output of a groupBy) statement.
transactionsDf.count("productId")
No. DataFrame.count() does not take any arguments.
transactionsDf.count("productId").distinct()
Wrong. Since DataFrame.count() does not take any arguments, this option cannot be right.
transactionsDf.groupBy("productId").agg(col("value").count())
False. A Column object, as returned by col("value"), does not have a count() method. You can see all available methods for Column object linked in the Spark documentation below.
More info: pyspark.sql.DataFrame.count - PySpark 3.1.2 documentation, pyspark.sql.Column - PySpark
3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

NEW QUESTION 26
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