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Databricks Machine Learning Associate Exam - Topic 3 Question 26 Discussion

A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.They have developed this code block to accomplish this task:The code block is returning an error.Which of the following adjustments does the data scientist need to make to accomplish this task?
C) They need to use Stringlndexer prior to one-hot encodinq the features.
A) They need to specify the method parameter to the OneHotEncoder.
B) They need to remove the line with the fit operation.
D) They need to use VectorAssembler prior to one-hot encoding the features.

Databricks Machine Learning Associate Exam - Topic 3 Question 26 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 26
Topic #: 3
[All Databricks Machine Learning Associate Questions]

A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.

They have developed this code block to accomplish this task:

The code block is returning an error.

Which of the following adjustments does the data scientist need to make to accomplish this task?

Show Suggested Answer Hide 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 Brock at Mar 04, 2025, 05:06 AM

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Ivette
7 months ago
I thought VectorAssembler was needed for this too!
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Francine
7 months ago
Removing the fit line? That doesn't seem right.
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Arminda
8 months ago
Wait, can you skip the fit operation? Sounds off.
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Luz
8 months ago
Totally agree with that! StringIndexer is essential.
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Hailey
8 months ago
They need to use StringIndexer prior to one-hot encoding.
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Rebbecca
8 months ago
I feel like VectorAssembler is more for combining features rather than one-hot encoding, so that option seems off to me.
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Bonita
9 months ago
I practiced a similar question, and I believe using StringIndexer is crucial for categorical features.
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Trina
9 months ago
I think the OneHotEncoder might need a method parameter, but I can't recall the exact syntax.
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Tonja
9 months ago
I remember something about needing to use StringIndexer before one-hot encoding, but I'm not entirely sure if that's the only step needed.
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Simona
9 months ago
I'm feeling pretty confident about this one. I think the solution is to specify the method parameter in the OneHotEncoder to handle the string columns properly.
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Cassi
9 months ago
Based on the options, it seems like we might need to use StringIndexer first to convert the string columns to numeric indices before one-hot encoding. That could be the issue.
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Melissia
9 months ago
I'm a bit confused by the OneHotEncoder setup. I wonder if we need to do some additional preprocessing on the data before applying it.
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Annmarie
9 months ago
Okay, let me think this through step-by-step. I think the key is to understand what the error is telling us and how we can fix it.
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Derick
9 months ago
Hmm, this looks like a tricky one. I'll need to carefully review the code and error message to figure out what's going on.
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Jill
9 months ago
Interesting, I wonder if we need to use VectorAssembler to combine the features into a single vector before one-hot encoding. That might be the key step we're missing.
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Mammie
9 months ago
Ah, I see. We might need to use StringIndexer first to convert the string columns to numeric indices before one-hot encoding them. That could be the issue.
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Eladia
9 months ago
Hmm, the fit operation seems unnecessary here since we're just trying to transform the data, not train a model. I'd try removing that line and see if that fixes the problem.
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Mel
9 months ago
I'm not entirely sure what the issue is here, but I think I might need to specify the method parameter to the OneHotEncoder to get this working.
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Thurman
1 year ago
Maybe the error is because they forgot to add the 'sparkly' parameter to the OneHotEncoder. You know, to make it extra fabulous.
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Shannan
1 year ago
I heard the data scientist tried to one-hot encode their socks. Turns out they were just a bunch of ones and zeros!
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Kenny
1 year ago
C: They need to use StringIndexer prior to one-hot encoding the features.
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Martha
1 year ago
B: They need to use VectorAssembler prior to one-hot encoding the features.
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Daniela
1 year ago
C: They need to use VectorAssembler prior to one-hot encoding the features.
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Delisa
1 year ago
B: They need to use StringIndexer prior to one-hot encoding the features.
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Hannah
1 year ago
A: They need to specify the method parameter to the OneHotEncoder.
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Wilburn
1 year ago
A: They need to specify the method parameter to the OneHotEncoder.
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Alfred
1 year ago
VectorAssembler? Sounds like a superhero name. Maybe that's the solution, but I'm not sure.
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Pura
1 year ago
User3: Maybe they need to use StringIndexer before one-hot encoding the features.
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Lonny
1 year ago
User2: No, they should specify the method parameter to the OneHotEncoder.
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Dick
1 year ago
User1: I think the data scientist needs to use VectorAssembler before one-hot encoding.
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Rosendo
1 year ago
Ah, I see the issue. The method parameter is missing from the OneHotEncoder. We need to specify that.
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Essie
1 year ago
User1: Let's add that parameter and see if it works.
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Joaquin
1 year ago
User2: Yes, that's correct. That should fix the error.
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Eden
1 year ago
User1: I think we need to specify the method parameter to the OneHotEncoder.
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Jenelle
1 year ago
Wait, I think we need to use StringIndexer first to convert the string columns to numerical values. Then we can use OneHotEncoder.
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Latrice
1 year ago
D: And OneHotEncoder will then encode those numerical values as binary vectors.
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Osvaldo
1 year ago
C: That makes sense, StringIndexer will convert the strings to numerical values.
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Whitley
1 year ago
B: Then we can use OneHotEncoder to encode the categorical features.
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Reita
1 year ago
A: I think you're right, we should use StringIndexer first.
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Ligia
1 year ago
I believe they should also use StringIndexer before one-hot encoding the features to properly encode the categorical values.
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Quentin
1 year ago
Hmm, the error is probably due to the fit operation. Let's try removing that line and see if it works.
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Lawrence
1 year ago
User2: Yeah, let's try that and see if it fixes the error.
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Adaline
1 year ago
User1: I think we should remove the line with the fit operation.
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Essie
1 year ago
I agree with Daisy. Without specifying the method parameter, the code won't work properly.
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Daisy
1 year ago
I think the data scientist needs to specify the method parameter to the OneHotEncoder.
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