Associate-Developer-Apache-Spark考試指南 - Databricks最新Associate-Developer-Apache-Spark題庫，Associate-Developer-Apache-Spark考古题推薦

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NEW QUESTION 31
Which of the following code blocks returns a copy of DataFrame transactionsDf in which column productId has been renamed to productNumber?

A. transactionsDf.withColumnRenamed(col(productId), col(productNumber))
B. transactionsDf.withColumnRenamed("productNumber", "productId")
C. transactionsDf.withColumnRenamed(productId, productNumber)
D. transactionsDf.withColumnRenamed("productId", "productNumber")
E. transactionsDf.withColumn("productId", "productNumber")

A. itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()
B. itemsDf.select(~col('supplier').contains('X')).distinct()
C. itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()
D. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()
E. itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()

Answer: D
Explanation:
Explanation
Output of correct code block:
+-------------------+
| supplier|
+-------------------+
|Sports Company Inc.|
+-------------------+
Key to managing this question is understand which operator to use to do the opposite of an operation

the ~ (not) operator. In addition, you should know that there is no unique() method.
Static notebook | Dynamic notebook: See test 1

NEW QUESTION 33
The code block displayed below contains an error. The code block should configure Spark to split data in 20 parts when exchanging data between executors for joins or aggregations. Find the error.
Code block:
spark.conf.set(spark.sql.shuffle.partitions, 20)

A. The code block is missing a parameter.
B. The code block sets the incorrect number of parts.
C. The code block sets the wrong option.
D. The code block uses the wrong command for setting an option.
E. The code block expresses the option incorrectly.

Answer: E
Explanation:
Explanation
Correct code block:
spark.conf.set("spark.sql.shuffle.partitions", 20)
The code block expresses the option incorrectly.
Correct! The option should be expressed as a string.
The code block sets the wrong option.
No, spark.sql.shuffle.partitions is the correct option for the use case in the question.
The code block sets the incorrect number of parts.
Wrong, the code block correctly states 20 parts.
The code block uses the wrong command for setting an option.
No, in PySpark spark.conf.set() is the correct command for setting an option.
The code block is missing a parameter.
Incorrect, spark.conf.set() takes two parameters.
More info: Configuration - Spark 3.1.2 Documentation

NEW QUESTION 34
......