Databricks Exam Databricks-Machine-Learning-Associate Topic 1 Question 3 Discussion

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

Question #: 3
Topic #: 1

[All Databricks-Machine-Learning-Associate Questions]

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

AKeras

BScikit-learn

CPyTorch

DSpark ML

Show Suggested Answer

Suggested Answer: D

Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark's distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets.

Databricks documentation on Spark MLlib: Spark MLlib

by Azzie at May 21, 2024, 08:41 AM

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Jenelle

11 months ago

Keras? More like 'ker-nope' for large-scale feature engineering. Spark ML is the clear winner here.

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Chauncey

10 months ago

Keras may be good for other things, but for large-scale feature engineering, Spark ML is the best choice.

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Merilyn

10 months ago

I've had success using Spark ML for distributing feature engineering tasks efficiently.

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Kristel

10 months ago

I agree, Spark ML is definitely the way to go for large-scale feature engineering.

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Hubert

11 months ago

PvTorch? Is that the latest version of PyTorch? I'll stick with the classics, thanks.

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Chun

10 months ago

I prefer sticking with the classics like Spark ML for machine learning pipelines.

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Pete

10 months ago

I think PvTorch is a new tool for distributing large-scale feature engineering.

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Elmer

10 months ago

I prefer sticking with the classics like Spark ML for large-scale feature engineering.

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Henriette

10 months ago

Yeah, I'm more comfortable using tools like Scikit-learn for machine learning pipelines.

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Sonia

10 months ago

I prefer sticking with the classics like Spark ML for large-scale feature engineering.

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Dulce

10 months ago

I think PvTorch is a new tool, not the latest version of PyTorch.

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Nadine

10 months ago

I think PvTorch is a new tool, not sure if it's the latest version of PyTorch.

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Regenia

11 months ago

Pandas? More like 'panda-monium' if you ask me. Spark ML is the real deal.

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Norah

11 months ago

I prefer using Scikit-learn for my machine learning pipelines.

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Nguyet

11 months ago

I agree, Spark ML is definitely the way to go for large-scale feature engineering.

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Lilli

11 months ago

Spark ML is the way to go for large-scale feature engineering! No need for those pesky UDFs or pandas.

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Frank

10 months ago

Spark ML is a game-changer when it comes to distributing feature engineering tasks.

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Denny

10 months ago

I prefer using Spark ML over other tools for large-scale feature engineering.

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Kristofer

10 months ago

Definitely, Spark ML simplifies the process of distributing feature engineering tasks.

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Amber

10 months ago

I think Spark ML is more efficient than using UDFs or pandas for feature engineering.

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Alpha

10 months ago

I agree, Spark ML makes it so much easier to distribute feature engineering tasks.

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Marleen

11 months ago

I agree, Spark ML makes it so much easier to distribute feature engineering tasks.

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Chandra

11 months ago

Spark ML is definitely the best choice for large-scale feature engineering.

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Alesia

11 months ago

Spark ML is definitely the best choice for large-scale feature engineering.

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