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

Explanation

Correct code block:

find_most_freq_letter_udf = udf(find_most_frequent_letter)

itemsDf.withColumn("most_frequent_letter", find_most_freq_letter_udf("itemName"))

Spark should use the previously registered find_most_freq_letter_udf method here – but it is not doing that in the original codeblock. There, it just uses the non-UDF version of the Python method.

Note that typically, we would have to specify a return type for udf(). Except in this case, since the default return type for udf() is a string which is what we are expecting here. If we wanted to return

an integer variable instead, we would have to register the Python function as UDF using find_most_freq_letter_udf = udf(find_most_freq_letter, IntegerType()).

More info: pyspark.sql.functions.udf — PySpark 3.1.1 documentation

Explanation

Correct code block:

spark.conf.set("spark.sql.shuffle.partitions", 100)

The conf interface is part of the SparkSession, so you need to call it through spark and not pyspark. To configure spark, you need to use the set method, not the get method. get reads a property,

but does not write it. The correct property to achieve what is outlined in the QUESTION NO: is spark.sql.aggregate.partitions, which needs to be passed to set as a string. Properties

spark.shuffle.partitions and spark.sql.aggregate.partitions do not exist in Spark.

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 45 (Databricks import instructions)

Explanation

This QUESTION NO: offers you a wide variety of answers for a seemingly simple question. However, this variety reflects the variety of ways that one can express a join in PySpark. You need to

understand

some SQL syntax to get to the correct answer here.

transactionsDf.createOrReplaceTempView('transactionsDf')

itemsDf.createOrReplaceTempView('itemsDf')

statement = """

SELECT * FROM transactionsDf

INNER JOIN itemsDf

ON transactionsDf.productId==itemsDf.itemId

"""

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

Correct - this answer uses SQL correctly to perform the inner join and afterwards drops the unwanted columns. This is totally fine. If you are unfamiliar with the triple-quote """ in Python: This allows

you to express strings as multiple lines.

transactionsDf \

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

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

No, this answer option is a trap, since DataFrame.drop() does not accept a list of Column objects. You could use transactionsDf.drop('value', 'storeId') instead.

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

Incorrect - Spark does not evaluate "transactionsDf.productId==itemsDf.itemId" as a valid join expression. This would work if it would not be a string.

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

Wrong, this statement incorrectly uses itemsDf.select instead of itemsDf.drop.

transactionsDf.createOrReplaceTempView('transactionsDf')

itemsDf.createOrReplaceTempView('itemsDf')

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

No, here the SQL expression syntax is incorrect. Simply specifying -columnName does not drop a column.

More info: pyspark.sql.DataFrame.join — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 25 (Databricks import instructions)