Databricks Exam Databricks Machine Learning Associate Topic 3 Question 10 Discussion

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

Question #: 10
Topic #: 3

[All Databricks Machine Learning Associate Questions]

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model in parallel. They elect to use the Hyperopt library to facilitate this process.

Which of the following Hyperopt tools provides the ability to optimize hyperparameters in parallel?

Afmin

BSparkTrials

Cquniform

Dsearch_space

Eobjective_function

Show Suggested Answer

Suggested Answer: A

In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers like StringIndexer, VectorAssembler, and StandardScaler.

Databricks documentation on transformers: Transformers in Spark ML

by Titus at Jul 07, 2024, 04:01 AM

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Corrina

2 months ago

Hmm, this question is making me feel like I need to brush up on my Hyperopt knowledge. Time to go binge-watch some scikit-learn tutorials!

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Huey

15 days ago

A) fmin

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Glendora

2 months ago

I'm leaning towards B) SparkTrials. Parallel hyperparameter tuning is a pretty specific use case, and that's what the question is focused on.

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Alfreda

2 months ago

Ooh, this is a good one! I'd say the answer is B) SparkTrials. It's the only option here that mentions parallel optimization, which is what the question is asking for.

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Hortencia

1 months ago

I'm not sure about the others, but C) quniform doesn't sound like it's for parallel optimization.

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Bettina

1 months ago

I would go with B) SparkTrials. It seems like the best option for parallel optimization.

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Chara

2 months ago

I think the answer is A) fmin. It sounds like a tool that could optimize hyperparameters efficiently.

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Chara

2 months ago

I'm pretty sure the answer is B) SparkTrials. Hyperopt has that built-in functionality for parallel tuning, right? I better double-check the docs just to be sure.

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Long

19 days ago

I remember reading that D) search_space is the tool in Hyperopt for parallel tuning.

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Erasmo

2 months ago

I'm not sure, but I think C) quniform might be the one for parallel hyperparameter optimization.

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Alisha

2 months ago

No, I believe it's B) SparkTrials that is used for parallel tuning in Hyperopt.

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Crista

2 months ago

I think it's actually A) fmin that allows for parallel optimization.

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Ira

3 months ago

I think both A) fmin and B) SparkTrials can be used for parallel optimization, depending on the specific requirements of the data scientist.

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Lizette

3 months ago

Hmm, this looks like a tricky one. I think the answer might be B) SparkTrials, since that's specifically designed for parallel hyperparameter optimization.

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Louis

2 months ago

Yes, SparkTrials is designed for parallel hyperparameter optimization. Good choice!

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Reta

2 months ago

I think you're right, B) SparkTrials is the correct answer for optimizing hyperparameters in parallel.

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Maybelle

3 months ago

I disagree, I believe the correct answer is A) fmin as it is used for optimizing hyperparameters.

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Erick

3 months ago

I think the answer is B) SparkTrials because it allows optimization in parallel.

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