Databricks Machine Learning Associate Question 348
Single answerYou are working on a Databricks notebook and want to process large-scale data using the Pandas on Spark API. How should you correctly import and use the Pandas on Spark library to ensure compatibility with your Spark cluster?
- A
Import the Pandas on Spark library using
import pandasand call the.to_spark()method on your DataFrame. - B
Import the Pandas on Spark library using
import pyspark.pandas as psand use it for large-scale data processing. - C
Import the Pandas on Spark library using
import pandas as pdand set an environment variable to enable Spark backend. - D
Import the Pandas on Spark library using
import pyspark.sql.pandas as pdand use it for distributed data operations.
Show answer and explanation
Correct answer: B
Explanation
To use Pandas on Spark in a Databricks environment, you must import it using import pyspark.pandas as ps. This library provides an API similar to Pandas but is explicitly designed to work on Spark, enabling distributed data processing across a cluster. Importing standard Pandas or incorrect modules will not leverage Spark’s capabilities.
- A. Incorrect.
This option is incorrect because the standard Pandas library (
import pandas) does not support distributed processing on Spark. The.to_spark()method does not exist in Pandas. - B. Correct.
This option is correct because the Pandas on Spark library is accessed through
pyspark.pandas, which provides an API similar to Pandas while leveraging Spark’s distributed computing capabilities. - C. Incorrect.
This option is incorrect because importing Pandas normally (
import pandas as pd) does not utilize Spark’s distributed processing. Setting an environment variable is not sufficient to enable the Spark backend. - D. Incorrect.
This option is incorrect because
pyspark.sql.pandasis not a valid import for Pandas on Spark. The correct import ispyspark.pandas.