Databricks Machine Learning Associate Question 349
Single answerYou are working on a Databricks notebook and need to process a large dataset using Pandas on Spark APIs to leverage distributed computing. How should you correctly import and initialize Pandas on Spark in your notebook?
- A
Import the Pandas on Spark module using 'import pyspark.pandas as ps'
- B
Import the Pandas on Spark module using 'from pyspark.sql import pandas_on_spark as ps'
- C
Initialize Pandas on Spark by setting 'spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")'
- D
Use 'import pandas as pd' and directly apply Pandas on Spark functions
Show answer and explanation
Correct answer: A
Explanation
To use Pandas on Spark APIs in a Databricks environment, you need to import the module 'pyspark.pandas' and can optionally alias it as 'ps'. This module provides a distributed computing version of Pandas functions. Incorrect imports or configurations like 'pandas_on_spark' or the standard Pandas library will not enable Pandas on Spark functionality.
- A. Correct.
This is the correct syntax to import Pandas on Spark, allowing you to use its APIs with the alias 'ps'.
- B. Incorrect.
This is incorrect because the 'pandas_on_spark' module does not exist in the 'pyspark.sql' package.
- C. Incorrect.
While enabling Arrow optimization can improve performance, it is not required to initialize or use Pandas on Spark APIs.
- D. Incorrect.
Using 'import pandas as pd' imports the standard Pandas library, not Pandas on Spark, so it does not allow you to use distributed Pandas on Spark functions.