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:

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)

Explanation

Correct code block:

transactionsDf.filter(col('predError').isin([3, 6])).count()

The isin method is the correct one to use here – the in method does not exist for the Column object.

More info: pyspark.sql.Column.isin — PySpark 3.1.2 documentation

Explanation

Correct code block:

transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir)

Solving this QUESTION NO: requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.

Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression

options are explained in the DataFrameWriter.parquet documentation linked below.

Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.

More info:

- DataFrame.write: pyspark.sql.DataFrame.write — PySpark 3.1.1 documentation

- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 58 (Databricks import instructions)