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:

transactionsDf.select((col("storeId").between(20, 30)) & (col("productId")==2))

Although this QUESTION NO: may make you think that it asks for a filter or where statement, it does not. It asks explicity to return a column with booleans – this should point you to the select

statement.

Another trick here is the rarely used between() method. It exists and resolves to ((storeId >= 20) AND (storeId <= 30)) in SQL. geq() and leq() do not exist.

Another riddle here is how to chain the two conditions. The only valid answer here is &. Operators like && or and are not valid. Other boolean operators that would be valid in Spark are | and.

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 42 (Databricks import instructions)

Explanation

Result of correct code block:

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

|attributes_exploded|

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

| winter|

| cooling|

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

To solve this question, you need to know about explode(). This operation helps you to split up arrays into single rows. If you did not have a chance to familiarize yourself with this method yet, find

more examples in the documentation (link below).

Note that explode() is a method made available through pyspark.sql.functions – it is not available as a method of a DataFrame or a Column, as written in some of the answer options.

More info: pyspark.sql.functions.explode — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 18 (Databricks import instructions)

Explanation

Correct code block:

spark.createDataFrame([("red",), ("blue",), ("green",)], ["color"])

The createDataFrame syntax is not exactly straightforward, but luckily the documentation (linked below) provides several examples on how to use it. It also shows an example very similar to the

code block presented here which should help you answer this QUESTION NO: correctly.

More info: pyspark.sql.SparkSession.createDataFrame — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 23 (Databricks import instructions)