Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.0 Exam

Explanation

Correct code block:

def count_to_target(target):

if target is None:

return

result = list(range(target))

return result

count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))

transactionsDf.select(count_to_target_udf('predError'))

Output of correct code block:

+--------------------------+

|count_to_target(predError)|

+--------------------------+

| [0, 1, 2]|

| [0, 1, 2, 3, 4, 5]|

| [0, 1, 2]|

| null|

| null|

| [0, 1, 2]|

+--------------------------+

This QUESTION NO: is not exactly easy. You need to be familiar with the syntax around UDFs (user-defined functions). Specifically, in this QUESTION NO: it is important to pass the correct types

to the udf

method - returning an array of a specific type rather than just a single type means you need to think harder about type implications than usual.

Remember that in Spark, you always pass types in an instantiated way like ArrayType(IntegerType()), not like ArrayType(IntegerType). The parentheses () are the key here - make sure you do not

forget those.

You should also pay attention that you actually pass the UDF count_to_target_udf, and not the Python method count_to_target to the select() operator.

Finally, null values are always a tricky case with UDFs. So, take care that the code can handle them correctly.

More info: How to Turn Python Functions into PySpark Functions (UDF) – Chang Hsin Lee – Committing my thoughts to words.

Static notebook | Dynamic notebook: See test 3, QUESTION NO: 24 (Databricks import instructions)

Explanation

The key to solving this QUESTION NO: is knowing (or reading in the documentation) that, by default, cache() stores values to memory and writes any partitions for which there is insufficient memory

to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option listed here. It is also worth noting that cache() does not have any arguments.

If you have troubles finding the storage level information in the documentation, please also see this student Q&A thread that sheds some light here.

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 30 (Databricks import instructions)

Explanation

Correct code block:

itemsDf.withColumn("itemNameElements", split("itemName"," "))

Output of code block:

+------+----------------------------------+-------------------+------------------------------------------+

|itemId|itemName |supplier |itemNameElements |

+------+----------------------------------+-------------------+------------------------------------------+

|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|[Thick, Coat, for, Walking, in, the, Snow]|

|2 |Elegant Outdoors Summer Dress |YetiX |[Elegant, Outdoors, Summer, Dress] |

|3 |Outdoors Backpack |Sports Company Inc.|[Outdoors, Backpack] |

+------+----------------------------------+-------------------+------------------------------------------+

The key to solving this QUESTION NO: is that the split method definitely needs a second argument here (also look at the link to the documentation below). Given the values in column itemName in

DataFrame itemsDf, this should be a space character " ". This is the character we need to split the words in the column.

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

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 46 (Databricks import instructions)