Certified Associate Developer for Apache Spark: DataFrames Casting Column Types

Learn how to manipulate DataFrame columns in Apache Spark, including casting column data types. Understand the use of withColumn, col, cast, and StringType functions through practical examples.

Question

The code block shown below should return a new DataFrame from DataFrame storesDF where column storeId is of the type string. Choose the response that correctly fills in the numbered blanks within the code block to complete this task.

Code block:

storesDF.__1__(“storeId”, __2__(“storeId”).__3__(__4__)

A. 1. withColumn
2. col
3. cast
4. StringType()
B. 1. withColumn
2. cast
3. col
4. StringType()
C. 1. newColumn
2. col
3. cast
4. StringType()
D. 1. withColumn
2. cast
3. col
4. StringType
E. 1. withColumn
2. col
3. cast
4. StringType

Answer

A. 1. withColumn
2. col
3. cast
4. StringType()

Explanation

The correct answer is Option A. The withColumn method is used to add a new column or replace an existing column in a DataFrame. The col function is used to return a column based on the given column name, storeId in this case. The cast function is used to convert the data type of the column, and StringType() specifies that the new data type should be string.

Here’s the completed code block:

storesDF.withColumn("storeId", col("storeId").cast(StringType()))

This code will return a new DataFrame where the storeId column has been cast to a string data type.

Databricks Certified Associate Developer for Apache Spark certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Databricks Certified Associate Developer for Apache Spark exam and earn Databricks Certified Associate Developer for Apache Spark certification.