Databricks Machine Learning Associate Exam - Topic 2 Question 36 Discussion

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

Question #: 36
Topic #: 2

[All Databricks Machine Learning Associate Questions]

A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.

The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

AThey need to call the transform method on train df

BThey need to convert the features column to be a vector

CThey do not need to make any changes

DThey need to utilize a Pipeline to fit the model

FThey need to split the features column out into one column for each feature

Show Suggested Answer

Suggested Answer: B

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

Reference

Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

by Laurel at Nov 20, 2025, 06:27 PM

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Tamie

2 months ago

Splitting features could be useful, but I think vectorizing is key.

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Rozella

2 months ago

I agree with Roslyn. A Pipeline streamlines the process.

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Roslyn

2 months ago

No way, a Pipeline is essential for fitting the model properly.

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Onita

2 months ago

I’m not sure. Maybe they don’t need to make any changes?

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Gaynell

2 months ago

Yeah, that makes sense. The model needs the right format.

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Sylvie

2 months ago

I thought they could just use the existing features without changes.

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Iola

3 months ago

A Pipeline would definitely help streamline the process!

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Margurite

3 months ago

Wait, do they really need to split the features column? Seems unnecessary.

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Cherry

3 months ago

Totally agree, option B is the way to go!

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Pearlene

4 months ago

Option C seems too easy. There's gotta be a catch somewhere. I'd double-check that code if I were the engineer.

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Sharmaine

4 months ago

Haha, I bet the engineer is wishing they had a crystal ball to see the right answer. Good thing we have these practice exams to prepare!

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Jose

4 months ago

I think Option D is the way to go. Using a Pipeline makes the whole process a lot cleaner and more organized.

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Elenore

4 months ago

I think splitting the features into separate columns could be an option, but it seems like overkill for a linear regression model.

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Lenna

4 months ago

I feel like we had a practice question about transforming DataFrames, but I can't recall if it was specifically about calling the transform method.

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Lizette

4 months ago

I'm not entirely sure, but I think using a Pipeline might be necessary for organizing the steps in the model training process.

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Whitney

5 months ago

I remember we discussed the importance of converting the features column into a vector format for Spark ML. That seems crucial here.

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Brent

5 months ago

Splitting the features column into individual columns seems like overkill for a linear regression model. I think I'll stick with option B and make sure the features column is a vector. That should do the trick.

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Ryan

5 months ago

I'm a little unsure about the Pipeline part. Should I be using a Pipeline to fit the model? Option D is making me second-guess myself. I'll need to research that a bit more.

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Magda

5 months ago

Option B looks good to me. Gotta get those features into a vector format for the model to work properly.

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Gertude

5 months ago

Okay, let me think this through. The code block looks good, so I don't think I need to make any changes. I'll go with option C and see if that works.

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Lemuel

5 months ago

They need to convert the features column to be a vector.

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Lauran

6 months ago

I think they need to convert the features column to be a vector.

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Royal

6 months ago

I'm a bit confused here. The code block shows that the features column is already a vector, so I'm not sure if I need to do anything else. Maybe I should double-check the documentation to be sure.

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Kimbery

6 months ago

Hmm, this looks like a pretty straightforward linear regression problem. I'd start by making sure the features column is a vector, so I'd go with option B.

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