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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions and Answers

Question # 1

The code block displayed below contains an error. The code block should use Python method find_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDf and

return it in a new column most_frequent_letter. Find the error.

Code block:

1. find_most_freq_letter_udf = udf(find_most_freq_letter)

2. itemsDf.withColumn("most_frequent_letter", find_most_freq_letter("itemName"))


Spark is not using the UDF method correctly.


The UDF method is not registered correctly, since the return type is missing.


The "itemName" expression should be wrapped in col().


UDFs do not exist in PySpark.


Spark is not adding a column.

Question # 2

The code block shown below should set the number of partitions that Spark uses when shuffling data for joins or aggregations to 100. Choose the answer that correctly fills the blanks in the code

block to accomplish this.


__1__.__2__.__3__(__4__, 100)


1. spark

2. conf

3. set

4. "spark.sql.shuffle.partitions"


1. pyspark

2. config

3. set

4. spark.shuffle.partitions


1. spark

2. conf

3. get

4. "spark.sql.shuffle.partitions"


1. pyspark

2. config

3. set

4. "spark.sql.shuffle.partitions"


1. spark

2. conf

3. set

4. "spark.sql.aggregate.partitions"

Question # 3

Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from

DataFrame transactionsDf and column attributes from DataFrame itemsDf?


transactionsDf.drop('value', 'storeId').join('attributes'), transactionsDf.productId==itemsDf.itemId)





4.spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes")


transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"), "transactionsDf.productId==itemsDf.itemId")


1.transactionsDf \

2. .drop(col('value'), col('storeId')) \

3. .join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))





4.statement = """

5.SELECT * FROM transactionsDf

6.INNER JOIN itemsDf

7.ON transactionsDf.productId==itemsDf.itemId


9.spark.sql(statement).drop("value", "storeId", "attributes")

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