Exam Databricks-Machine-Learning-Associate Topic 2 Question 39 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 39
Topic #: 2
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?

Suggested Answer: B Vote an answer

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.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark

by Roderick at Oct 05, 2026, 12:16 AM

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