Databricks Exam Databricks Machine Learning Associate Topic 2 Question 29 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam

Question #: 29
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?

AThe vectorized pandas UDFs allow for the use of type hints

BThe vectorized pandas UDFs process data in batches rather than one row at a time

CThe vectorized pandas UDFs allow for pandas API use inside of the function

DThe vectorized pandas UDFs work on distributed DataFrames

EThe vectorized pandas UDFs process data in memory rather than spilling to disk

Show Suggested 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

by Han at May 04, 2025, 11:25 PM

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Tiffiny

21 days ago

That's true, using pandas API can make data manipulation easier and more efficient.

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Nell

25 days ago

I believe another benefit is that vectorized pandas UDFs allow for pandas API use inside of the function.

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Kris

27 days ago

I agree with Malika, processing data in batches can improve performance.

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Malika

1 months ago

I think the benefit of using vectorized pandas UDFs is that they process data in batches rather than one row at a time.

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Brynn

1 months ago

Hold up, are we talking about vectorized pandas UDFs or some kind of super-charged vacuum cleaners? I'm so confused, but I'm all for anything that makes my data processing more efficient!

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Marilynn

1 months ago

D) The vectorized pandas UDFs work on distributed DataFrames, which means I can scale up my processing power. More cores, more speed!

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Refugia

17 days ago

A) The vectorized pandas UDFs allow for the use of type hints

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Jacklyn

2 months ago

E) The vectorized pandas UDFs process data in memory rather than spilling to disk, which is super important for large datasets. No more waiting for I/O!

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Cruz

16 days ago

C) The vectorized pandas UDFs allow for pandas API use inside of the function

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Leigha

17 days ago

B) The vectorized pandas UDFs process data in batches rather than one row at a time

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Talia

1 months ago

A) The vectorized pandas UDFs allow for the use of type hints

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Janessa

2 months ago

C) The vectorized pandas UDFs allow for pandas API use inside of the function, which is a game-changer. I can use all my favorite pandas tricks without having to convert back and forth.

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Chauncey

2 months ago

B) The vectorized pandas UDFs process data in batches rather than one row at a time, which is a huge performance boost! I love it when my code runs faster.

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