Associate-Developer-Apache-Spark最も有効な質問と解答で勉強

Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?

• A. transactionsDf.sort(col("value").asc()).print(10)
• B. transactionsDf.sort(asc(value)).show(10)
• C. transactionsDf.sort(col("value").desc()).head()
• D. transactionsDf.sort(col("value")).show(10)
• E. transactionsDf.orderBy("value").asc().show(10)

In which order should the code blocks shown below be run in order to return the number of records that are not empty in column value in the DataFrame resulting from an inner join of DataFrame transactionsDf and itemsDf on columns productId and itemId, respectively?
1. .filter(~isnull(col('value')))
2. .count()
3. transactionsDf.join(itemsDf, col("transactionsDf.productId")==col("itemsDf.itemId"))
4. transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId, how='inner')
5. .filter(col('value').isnotnull())
6. .sum(col('value'))

• A. 3, 1, 2
• B. 3, 1, 6
• C. 3, 5, 2
• D. 4, 1, 2
• E. 4, 6

Which of the following code blocks returns a new DataFrame in which column attributes of DataFrame itemsDf is renamed to feature0 and column supplier to feature1?

• A. itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1")
• B. itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1"))
• C. itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1")
• D. itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1)
• E. 1.itemsDf.withColumnRenamed("attributes", "feature0")
  2.itemsDf.withColumnRenamed("supplier", "feature1")

Which of the following describes tasks?

• A. A task is a command sent from the driver to the executors in response to a transformation.
• B. A task is a collection of slots.
• C. Tasks transform jobs into DAGs.
• D. Tasks get assigned to the executors by the driver.
• E. A task is a collection of rows.

Which of the following code blocks applies the boolean-returning Python function evaluateTestSuccess to column storeId of DataFrame transactionsDf as a user-defined function?

• A. 1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
  2.transactionsDf.withColumn("result", evaluateTestSuccessUDF(storeId))
• B. 1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
  2.transactionsDf.withColumn("result", evaluateTestSuccessUDF(col("storeId")))
• C. 1.from pyspark.sql import types as T
  2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.IntegerType())
  3.transactionsDf.withColumn("result", evaluateTestSuccess(col("storeId")))
• D. 1.from pyspark.sql import types as T
  2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
  3.transactionsDf.withColumn("result", evaluateTestSuccessUDF(col("storeId")))
• E. 1.from pyspark.sql import types as T
  2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
  3.transactionsDf.withColumn("result", evaluateTestSuccess(col("storeId")))