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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?

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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
11 months ago
I've had success using Spark ML for distributing feature engineering tasks efficiently.
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Kristel
11 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
11 months ago
I think PvTorch is a new tool, not sure if it's the latest version of PyTorch.
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Regenia
12 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
12 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
11 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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