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Databricks Exam Databricks-Machine-Learning-Associate Topic 1 Question 21 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 21
Topic #: 1
[All Databricks-Machine-Learning-Associate Questions]

Which of the following statements describes a Spark ML estimator?

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Suggested Answer: D

In the context of Spark MLlib, an estimator refers to an algorithm which can be 'fit' on a DataFrame to produce a model (referred to as a Transformer), which can then be used to transform one DataFrame into another, typically adding predictions or model scores. This is a fundamental concept in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to produce transformers.

Reference

Spark MLlib Documentation: https://spark.apache.org/docs/latest/ml-pipeline.html#estimators


Contribute your Thoughts:

Shawn
5 months ago
Hmm, that makes sense too. I guess we'll have to review the material again to be sure.
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Sabra
5 months ago
I disagree, I believe the answer is D. It mentions fitting an algorithm on a DataFrame to produce a Transformer.
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Shawn
5 months ago
I think the answer is B, because it mentions chaining multiple algorithms together.
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Vashti
5 months ago
Ha, this question is a real spark-ler! Get it? Spark? Anyway, I think D is the way to go. Estimators are like the superheroes of Spark ML, turning DataFrames into Transformers with a single bound!
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Lonny
4 months ago
D) An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
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Dante
4 months ago
B) An estimator chains multiple algorithms together to specify an ML workflow
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Yvonne
5 months ago
A) An estimator is a hyperparameter that can be used to train a model
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Evangelina
5 months ago
Hmm, I'm not sure about this one. But I know that estimators are definitely not hyperparameters, so I can rule out option A. Maybe I'll just go with D and hope for the best!
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Franchesca
4 months ago
Jaime: Sounds good, let's go with D and see what happens.
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Armanda
5 months ago
User 3: I'm going with option D, it seems like a good choice.
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Jaime
5 months ago
User 2: I agree, let's rule out option A.
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Mickie
5 months ago
User 1: I think you're right, estimators are not hyperparameters.
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Margurite
5 months ago
Option C seems like the right choice. An estimator is a trained ML model that can take a DataFrame with features and output a DataFrame with predictions.
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Dacia
5 months ago
I think option D is the correct answer. An estimator is an algorithm that can be fit on a DataFrame to produce a Transformer, which then can be used to transform new data.
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Ammie
4 months ago
I'm not sure, but option C also sounds plausible, turning features into predictions.
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Wava
4 months ago
I believe it's option A, an estimator being a hyperparameter makes sense to me.
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Anika
5 months ago
I think it might be option B, chaining multiple algorithms together sounds like what an estimator does.
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Derrick
5 months ago
I agree with you, option D is the correct answer.
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