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Databricks Machine Learning Associate Exam - Topic 2 Question 35 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 35
Topic #: 2
[All Databricks Machine Learning Associate Questions]

Which statement describes a Spark ML transformer?

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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 Shala at Nov 22, 2025, 03:50 AM

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Denny
6 days ago
D is also off. A transformer doesn’t train a model directly.
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Stephaine
12 days ago
I feel like B is misleading. Hyperparameter grids are different.
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Jani
17 days ago
But isn’t A more straightforward? C feels a bit complex.
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Ciara
22 days ago
C sounds interesting too. Chaining algorithms is a key part of ML.
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Floyd
27 days ago
Definitely A! It’s the core of how Spark ML works!
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Raul
2 months ago
I thought transformers were for training models, not just transforming data.
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Verdell
2 months ago
Wait, isn't a transformer just for chaining algorithms?
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Lyla
2 months ago
Totally agree, it's all about transforming data!
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Annice
2 months ago
A transformer changes one DataFrame to another, right?
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Devora
2 months ago
Haha, a transformer is like a Transformer from Transformers - it just changes one thing into another! Spark ML must be pretty cool.
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Shawnta
2 months ago
A transformer is a learning algorithm that trains a model on a DataFrame. Isn't that what Spark ML is all about?
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Onita
3 months ago
I'm pretty sure a transformer is a hyperparameter grid that you use to tune your model. That's how you get the best performance.
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Jamal
3 months ago
A transformer is definitely an algorithm that can transform one DataFrame into another. That's the whole point of Spark ML, right?
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Brock
3 months ago
I thought transformers were more about chaining algorithms, so I might lean towards C, but I'm not completely confident.
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Lai
3 months ago
I practiced a question like this, and I feel like transformers are related to algorithms, but I can't recall if it's A or D.
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Tora
3 months ago
I think a transformer is something that changes one DataFrame into another, so I might go with A.
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Corinne
3 months ago
I'm pretty confident the answer is A. Transformers are a core part of the Spark ML library, and their main purpose is to transform DataFrames as part of the ML pipeline. The other options don't seem to match that definition as well.
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Norah
4 months ago
I agree, A makes the most sense. It’s about transforming DataFrames.
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Janessa
4 months ago
Option C sounds interesting - a transformer that chains multiple algorithms together. That could be a useful way to build complex ML workflows. But I'm not 100% sure if that's the best description here.
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Alease
4 months ago
I think A is correct. It describes the basic function of a transformer.
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Johna
4 months ago
Nah, a transformer chains multiple algorithms together. That's how you build complex ML workflows in Spark.
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Lajuana
4 months ago
I'm not entirely sure, but I remember something about transformers being part of the ML workflow, maybe C?
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Virgina
5 months ago
Hmm, I'm a bit confused on this one. I know transformers are an important part of Spark ML, but I'm not sure if the other options are completely wrong. I might need to review my notes on transformers before answering this.
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Cherilyn
5 months ago
I think the answer is A. A transformer is an algorithm that can transform one DataFrame into another DataFrame. That sounds like the most accurate description to me.
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Pura
1 day ago
I agree, A seems right. It’s straightforward.
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