Databricks Machine Learning Associate Question 350
Select 3You are working on a Databricks notebook and need to process a large dataset using Pandas on Spark APIs to handle scalability. Which of the following steps are required to import and use Pandas on Spark APIs correctly in Databricks?
- A
Import the Pandas on Spark API using 'import pyspark.pandas as ps'.
- B
Use 'import pandas as pd' to access Pandas on Spark APIs.
- C
Ensure the Spark session is active before using Pandas on Spark APIs.
- D
Directly call Spark DataFrame methods without importing the Pandas on Spark module.
- E
Use Pandas on Spark's API by calling methods like 'ps.DataFrame()' and 'ps.read_csv()'.
Show answer and explanation
Correct answers: A, C, E
Explanation
To use Pandas on Spark APIs in Databricks, you must import the 'pyspark.pandas' module, ensure an active Spark session, and call Pandas on Spark API methods like 'ps.DataFrame()' and 'ps.read_csv()'. These steps are necessary to leverage the scalability of Pandas on Spark for large dataset processing.
- A. Correct.
Correct: The Pandas on Spark API is accessed by importing 'pyspark.pandas' as 'ps'. This is the standard way to enable Pandas on Spark in Databricks.
- B. Incorrect.
Incorrect: 'import pandas as pd' is for the standard Pandas library, not Pandas on Spark. This will not allow you to use the Pandas on Spark APIs.
- C. Correct.
Correct: Pandas on Spark relies on an active Spark session for distributed processing, which is automatically available in Databricks notebooks.
- D. Incorrect.
Incorrect: While Spark DataFrame methods can be used in Spark, Pandas on Spark APIs require explicit imports and are not directly available without using 'pyspark.pandas'.
- E. Correct.
Correct: Pandas on Spark APIs can be used by calling methods like 'ps.DataFrame()' and 'ps.read_csv()', which mirror standard Pandas API but are optimized for distributed computation.