Vendor: Databricks
Exam Code: DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0
Certification: Databricks Certifications
Total Questions: 180 Q&A
Updated on: Jun 19, 2026
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The code block shown below should return the number of columns in the CSV file stored at location filePath. From the CSV file, only lines should be read that do not start with a # character. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
__1__(__2__.__3__.csv(filePath, __4__).__5__)
A. 1. size
2.
spark
3.
read()
4.
escape='#'
5.
columns
B. 1. DataFrame
2.
spark
3.
read()
4.
escape='#'
5.
shape[0]
C. 1. len
2.
pyspark
3.
DataFrameReader
4.
comment='#'
5.
columns
D. 1. size
2.
pyspark
3.
DataFrameReader
4.
comment='#'
5.
columns
E. 1. len
2.
spark
3.
read
4.
comment='#'
5.
columns
Which of the following describes properties of a shuffle?
A. Operations involving shuffles are never evaluated lazily.
B. Shuffles involve only single partitions.
C. Shuffles belong to a class known as "full transformations".
D. A shuffle is one of many actions in Spark.
E. In a shuffle, Spark writes data to disk.
The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-----------------------------+-------------------+
2.|itemId|itemName |attributes |supplier |
3.+------+----------------------------------+-----------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.| 5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |
6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|
7.+------+----------------------------------+-----------------------------+-------------------+
Code block:
itemsDf.__1__(__2__).select(__3__, __4__) A. 1. filter
2.
col("supplier").isin("Sports")
3.
"itemName"
4.
explode(col("attributes"))
B. 1. where
2.
col("supplier").contains("Sports")
3.
"itemName"
4.
"attributes"
C. 1. where
2.
col(supplier).contains("Sports")
3.
explode(attributes)
4.
itemName
D. 1. where
2.
"Sports".isin(col("Supplier"))
3.
"itemName"
4.
array_explode("attributes")
E. 1. filter
2.
col("supplier").contains("Sports")
3.
"itemName"
4.
explode("attributes")
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