Databricks Machine Learning Associate Exam - 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?

AAn estimator is a hyperparameter arid that can be used to train a model

BAn estimator chains multiple alqorithms toqether to specify an ML workflow

CAn estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions

DAn estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer

EAn estimator is an evaluation tool to assess to the quality of a model

Show Suggested Answer

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

by Becky at Nov 14, 2024, 11:24 AM

Limited Time Offer

25%

Off

Get Premium Databricks Machine Learning Associate Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Nobuko

4 months ago

I’m surprised by how many people mix up estimators and transformers!

upvoted 0 times

...

Barb

5 months ago

I thought estimators were just for hyperparameters, not models.

upvoted 0 times

...

Malcolm

5 months ago

Wait, isn't an estimator just a trained model?

upvoted 0 times

...

Felicitas

5 months ago

Totally agree, D is the right choice!

upvoted 0 times

...

Kattie

5 months ago

An estimator can be fit on a DataFrame to produce a Transformer.

upvoted 0 times

...

Galen

5 months ago

I vaguely recall that an estimator is used to train a model, but I can't remember if it directly produces predictions or if that's the role of a transformer.

upvoted 0 times

...

Chantay

6 months ago

I’m a bit confused about the definitions. I thought an estimator was more about the model itself rather than just an algorithm.

upvoted 0 times

...

Brynn

6 months ago

I remember practicing a question about how estimators and transformers work together. I feel like option D might be the right choice since it mentions fitting on a DataFrame.

upvoted 0 times

...

Lottie

6 months ago

I think an estimator is something that can be fit on a DataFrame, but I'm not sure if it's just a hyperparameter or something else.

upvoted 0 times

...

Yuette

6 months ago

Okay, I remember learning about estimators in class. An estimator is an algorithm that can be trained on data to produce a model. That matches option D, so that's my answer.

upvoted 0 times

...

Shakira

6 months ago

I'm not entirely sure about this. The options seem to describe different aspects of Spark ML, but I'm not sure which one specifically defines an estimator. I'll have to guess and hope for the best.

upvoted 0 times

...

Gayla

6 months ago

I'm pretty confident about this one. An estimator is an algorithm that can be fit on data to produce a model, which is then used as a Transformer to make predictions. I'm going with option D.

upvoted 0 times

...

Glendora

6 months ago

Hmm, I'm a bit confused by the different options. I'll need to think this through carefully. Maybe I should review my notes on Spark ML estimators before answering.

upvoted 0 times

...

Paris

6 months ago

This question seems straightforward. I think the correct answer is D - an estimator is an algorithm that can be fit on a DataFrame to produce a Transformer.

upvoted 0 times

...

Shawn

1 year ago

Hmm, that makes sense too. I guess we'll have to review the material again to be sure.

upvoted 0 times

...

Sabra

1 year ago

I disagree, I believe the answer is D. It mentions fitting an algorithm on a DataFrame to produce a Transformer.

upvoted 0 times

...

Shawn

1 year ago

I think the answer is B, because it mentions chaining multiple algorithms together.

upvoted 0 times

...

Vashti

1 year 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!

upvoted 0 times

Lonny

1 year ago

D) An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer

upvoted 0 times

...

Dante

1 year ago

B) An estimator chains multiple algorithms together to specify an ML workflow

upvoted 0 times

...

Yvonne

1 year ago

A) An estimator is a hyperparameter that can be used to train a model

upvoted 0 times

...

Evangelina

1 year 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!

upvoted 0 times

Franchesca

1 year ago

Jaime: Sounds good, let's go with D and see what happens.

upvoted 0 times

...

Armanda

1 year ago

User 3: I'm going with option D, it seems like a good choice.

upvoted 0 times

...

Jaime

1 year ago

User 2: I agree, let's rule out option A.

upvoted 0 times

...

Mickie

1 year ago

User 1: I think you're right, estimators are not hyperparameters.

upvoted 0 times

...

Margurite

1 year 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.

upvoted 0 times

...

Dacia

1 year 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.

upvoted 0 times