Databricks Exam Databricks-Machine-Learning-Associate Topic 4 Question 14 Discussion

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

Question #: 14
Topic #: 4

[All Databricks-Machine-Learning-Associate Questions]

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

ALogistic regression

BSingular value decomposition

CIterative optimization

DLeast-squares method

Show Suggested Answer

Suggested Answer: C

For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden--Fletcher--Goldfarb--Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.

Databricks documentation on linear regression: Linear Regression in Spark ML

by Vivan at Sep 09, 2024, 11:56 AM

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Sheridan

7 months ago

This question is a real head-scratcher. I'm going to go with C) Iterative optimization, but I hope the exam doesn't get 'linear' with these types of questions!

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Caprice

6 months ago

Yeah, it's important to have a method that can handle the scale of the data.

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Dick

6 months ago

I agree, that seems like the best approach for large datasets.

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Youlanda

6 months ago

I think C) Iterative optimization is the way to go.

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Franchesca

7 months ago

D) Least-squares method seems like a reasonable option, but I'm not sure if it's the specific technique used by Spark ML for this problem.

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Filiberto

6 months ago

B) Spark ML can distribute linear regression training using iterative optimization.

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Desirae

7 months ago

E) Singular value decomposition is not the approach used by Spark ML for distributing the training of a linear regression model.

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Susy

7 months ago

D) Least-squares method is a common technique for linear regression, but Spark ML uses iterative optimization for large datasets.

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Tandra

7 months ago

C) Iterative optimization is the approach used by Spark ML for distributing the training of a linear regression model.

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Tiffiny

8 months ago

I'm not sure, but I think Spark ML cannot distribute linear regression training.

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Florinda

8 months ago

C) Iterative optimization sounds like the right approach to me. It's more scalable for large datasets compared to the matrix decomposition methods.

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Lilli

7 months ago

Yeah, it's definitely more scalable for large datasets.

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Eulah

7 months ago

I think C) Iterative optimization is the way to go for distributing linear regression training in Spark ML.

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Daniela

8 months ago

E) Singular value decomposition is an interesting choice, but I don't think it's the most efficient approach for distributed linear regression training in Spark ML.

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Jeffrey

8 months ago

I agree with Alisha, iterative optimization is a common approach for distributed training in Spark ML.

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Alisha

8 months ago

I think Spark ML uses iterative optimization to distribute the training of a linear regression model for large data.

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