Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

Databricks Machine Learning Associate Exam - Topic 2 Question 41 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 41
Topic #: 2
[All Databricks Machine Learning Associate Questions]

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

Show Suggested Answer Hide Answer
Suggested Answer: B

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much more efficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.

Reference

PySpark Documentation on UDFs: https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs


Contribute your Thoughts:

0/2000 characters
Fatima
18 hours ago
I'm not entirely sure, but I feel like the ability to use the pandas API inside the function is a key feature. Could that be C?
upvoted 0 times
...
Annita
6 days ago
I think I remember that vectorized pandas UDFs process data in batches, which is a big advantage over standard UDFs. So, maybe B?
upvoted 0 times
...

Save Cancel