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Databricks Exam Databricks-Machine-Learning-Associate Topic 1 Question 5 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 5
Topic #: 1
[All Databricks-Machine-Learning-Associate Questions]

A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.

Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

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Suggested Answer: B

The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark's distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.


Databricks documentation on pandas API on Spark: pandas API on Spark

by Staci at May 23, 2024, 04:35 AM

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Hermila
10 months ago
Wait, we're supposed to be working with big data, not big egos. Let's keep this professional, folks.
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Effie
10 months ago
I heard pandas had a secret side gig as a bodybuilder - that's why the notebook is running so slow!
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Rosio
10 months ago
This is a tough one, but I think I'd go with PySpark DataFrame API. It's the most straightforward option and should give the data scientist the most bang for their buck.
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Leonida
10 months ago
I've heard that using pandas API on Spark can also help speed up processing time.
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Francoise
10 months ago
I think Spark SQL could also be a good option for optimizing the notebook's runtime.
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Trina
10 months ago
I agree, PySpark DataFrame API is a great choice for scaling with big data.
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Brunilda
11 months ago
Feature Store sounds interesting, but I'm not sure it's the best fit for this specific use case. Probably better for production-ready models.
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Anna
10 months ago
C: I agree, Feature Store might not be the best fit for this particular situation.
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Andra
10 months ago
B: Spark SQL might also help speed up processing time for large datasets.
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Mireya
10 months ago
A: PySpark DataFrame API could be a good option to handle big data efficiently.
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Lindsay
11 months ago
Spark SQL all the way! It's the perfect SQL-based solution for big data processing, and it integrates seamlessly with the rest of the Spark ecosystem.
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Odette
10 months ago
I agree, Spark SQL is a powerful tool that can handle large datasets efficiently.
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Theodora
10 months ago
Spark SQL is definitely the way to go. It's optimized for big data processing.
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Kasandra
10 months ago
C) Spark SQL
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Craig
10 months ago
A) PySpark DataFrame API
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Kattie
10 months ago
C: I think pandas API on Spark could work well too, but Spark SQL is definitely a strong choice.
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Wilda
10 months ago
B: PySpark DataFrame API might also be a good option for scaling with big data.
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Catalina
10 months ago
A: I agree, Spark SQL is the way to go for processing big data efficiently.
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Kerry
11 months ago
I'd say pandas API on Spark. It's the best of both worlds - the simplicity of pandas with the scalability of Spark.
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Salena
11 months ago
Definitely PySpark DataFrame API - it's designed to handle big data with ease, and it's a natural extension of the pandas API I'm already familiar with.
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Margart
10 months ago
I think PySpark DataFrame API is the best choice for minimizing refactoring efforts and scaling with big data.
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Victor
11 months ago
Yes, using PySpark will definitely save time and effort in refactoring the notebook for big data processing.
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Tamekia
11 months ago
I agree, PySpark DataFrame API is the way to go for handling big data efficiently.
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Val
11 months ago
Feature Store could be useful for managing and sharing features across different pipelines.
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Isreal
11 months ago
Spark SQL is another tool that can help optimize performance when dealing with large datasets.
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King
11 months ago
I've heard that using pandas API on Spark can also be a good option for scaling with big data.
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Tamera
11 months ago
I agree, PySpark DataFrame API is the way to go for handling big data efficiently.
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