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Databricks Machine Learning Associate Exam - Topic 1 Question 46 Discussion

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?
D) Spark ML
A) Keras
B) pandas
C) PvTorch
E) Scikit-learn

Databricks Machine Learning Associate Exam - Topic 1 Question 46 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 46
Topic #: 1
[All Databricks Machine Learning Associate Questions]

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

Show Suggested Answer Hide Answer
Suggested Answer: D

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios. Reference:

Spark MLlib documentation (Feature Engineering with Spark ML).


by Son at Jun 28, 2026, 09:15 AM

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Caprice
1 day ago
Exactly! Spark ML handles big data efficiently.
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Noble
6 days ago
Keras is more for deep learning, not large-scale distribution.
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Mona
11 days ago
Agreed! Spark ML scales well for large datasets.
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Leanora
17 days ago
True, but Keras has great features too.
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Precious
22 days ago
I think D) Spark ML is the best choice.
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Cordie
27 days ago
But Spark ML is designed for distributed computing.
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Vanda
1 month ago
I’m leaning towards C) PvTorch. It’s powerful for feature engineering.
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Hoa
1 month ago
Agreed! Spark ML scales well for large datasets.
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Caprice
1 month ago
I think D) Spark ML is the best choice.
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Annice
2 months ago
I still think pandas has its place, but not for large-scale tasks like this.
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Harrison
2 months ago
Wait, can you really use Spark ML without UDFs? Sounds too good to be true.
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Graham
2 months ago
Totally agree, Spark ML is built for that!
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Madelyn
2 months ago
I thought Keras was for deep learning, not feature engineering?
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Maryann
2 months ago
Spark ML is definitely the way to go for large-scale feature engineering!
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Halina
2 months ago
I feel like Scikit-learn is great for small datasets, but for large-scale feature engineering, Spark ML seems more appropriate.
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Quiana
3 months ago
I’m a bit confused about PvTorch; I don’t recall it being mentioned in our materials. Could it be a trick option?
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Carrol
3 months ago
I remember practicing with similar questions, and I think Keras is more focused on deep learning rather than feature engineering.
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Mel
3 months ago
I think Spark ML might be the right choice since it's designed for distributed computing, but I'm not entirely sure.
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