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

Explanation

Caching means storing a copy of a partition on an executor, so it can be accessed quicker by subsequent operations, instead of having to be recalculated. cache() is a lazily-evaluated method of the

DataFrame. Since count() is an action (while filter() is not), it triggers the caching process.

More info: pyspark.sql.DataFrame.cache — PySpark 3.1.2 documentation, Learning Spark, 2nd Edition, Chapter 7

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 20 (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)