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Databricks Machine Learning Associate Exam - 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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Janine
3 months ago
Totally agree, Spark ML is built for this kind of task!
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Nada
3 months ago
Wait, can PyTorch really do that without UDFs? Sounds odd.
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Rosita
3 months ago
Scikit-learn is great, but not for large-scale stuff.
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Scot
4 months ago
I thought Keras could handle that too?
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Brunilda
4 months ago
Spark ML is definitely the way to go for large-scale feature engineering!
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Malinda
4 months ago
I’m a bit confused about Scikit-learn's capabilities in this context; I thought it was more for smaller datasets.
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Sharita
4 months ago
I practiced a similar question where Spark ML was highlighted as the go-to for large-scale data processing. That might help here.
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Isreal
4 months ago
I think Keras and PyTorch are more focused on model building rather than feature engineering, but I could be wrong.
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Trina
5 months ago
I remember Spark ML being mentioned in our lectures as a good option for distributed feature engineering, but I'm not entirely sure if it's the only one.
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Clare
5 months ago
I'm leaning towards Spark ML as well. It's the only option that specifically mentions handling large-scale feature engineering without a UDF or pandas.
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Vilma
5 months ago
Spark ML seems like the obvious choice here. It's designed for large-scale data processing and has built-in support for feature engineering.
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Audry
5 months ago
I'm a bit confused by the question. Are we supposed to be looking for a framework that can distribute the feature engineering, or one that doesn't require a UDF or pandas?
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Tiffiny
5 months ago
I'm pretty sure Spark ML can handle large-scale feature engineering without using a UDF or pandas. I'll go with that.
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Georgeanna
5 months ago
Hmm, this is a tricky one. I'll need to think carefully about the different machine learning frameworks and their capabilities.
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Tracey
5 months ago
Okay, let me see if I can break this down. Risk is determined by the likelihood of the vulnerability being exploited and the potential impact if it is. So it makes sense that the formula would be Likelihood * Impact. I'm feeling good about going with option A.
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Casie
5 months ago
This is a tricky one, but I think I know the answer. The key is integrating the 'Approved Cost' budget with the General Ledger, while also allowing project managers to maintain the 'Staffing Plan' budget. I'm leaning towards option C.
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Jenelle
2 years ago
Keras? More like 'ker-nope' for large-scale feature engineering. Spark ML is the clear winner here.
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Chauncey
2 years 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
2 years ago
I've had success using Spark ML for distributing feature engineering tasks efficiently.
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Kristel
2 years ago
I agree, Spark ML is definitely the way to go for large-scale feature engineering.
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Hubert
2 years ago
PvTorch? Is that the latest version of PyTorch? I'll stick with the classics, thanks.
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Chun
2 years ago
I prefer sticking with the classics like Spark ML for machine learning pipelines.
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Pete
2 years ago
I think PvTorch is a new tool for distributing large-scale feature engineering.
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Elmer
2 years ago
I prefer sticking with the classics like Spark ML for large-scale feature engineering.
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Henriette
2 years ago
Yeah, I'm more comfortable using tools like Scikit-learn for machine learning pipelines.
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Sonia
2 years ago
I prefer sticking with the classics like Spark ML for large-scale feature engineering.
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Dulce
2 years ago
I think PvTorch is a new tool, not the latest version of PyTorch.
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Nadine
2 years ago
I think PvTorch is a new tool, not sure if it's the latest version of PyTorch.
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Regenia
2 years ago
Pandas? More like 'panda-monium' if you ask me. Spark ML is the real deal.
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Norah
2 years ago
I prefer using Scikit-learn for my machine learning pipelines.
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Nguyet
2 years ago
I agree, Spark ML is definitely the way to go for large-scale feature engineering.
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Lilli
2 years 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
2 years ago
Spark ML is a game-changer when it comes to distributing feature engineering tasks.
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Denny
2 years ago
I prefer using Spark ML over other tools for large-scale feature engineering.
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Kristofer
2 years ago
Definitely, Spark ML simplifies the process of distributing feature engineering tasks.
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Amber
2 years ago
I think Spark ML is more efficient than using UDFs or pandas for feature engineering.
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Alpha
2 years ago
I agree, Spark ML makes it so much easier to distribute feature engineering tasks.
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Marleen
2 years ago
I agree, Spark ML makes it so much easier to distribute feature engineering tasks.
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Chandra
2 years ago
Spark ML is definitely the best choice for large-scale feature engineering.
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Alesia
2 years ago
Spark ML is definitely the best choice for large-scale feature engineering.
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