Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 343 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 343

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You are working on a machine learning project in Databricks and need to convert a PySpark DataFrame to a Pandas on Spark DataFrame to leverage some pandas-like operations. However, you also want to ensure that the conversion does not load the entire dataset into the driver’s memory, as the dataset is large. Which of the following methods can you use to safely perform this conversion?

  1. A

    Use the .to_pandas() method on the PySpark DataFrame.

  2. B

    Use the ps.from_pandas() function with a Pandas DataFrame.

  3. C

    Use the ps.DataFrame() constructor with the PySpark DataFrame.

  4. D

    Use the .to_pandas_on_spark() method on the PySpark DataFrame.

Show answer and explanation

Correct answers: C, D

Explanation

When converting a PySpark DataFrame to a Pandas on Spark DataFrame, it is important to use methods that handle large datasets efficiently without overloading the driver’s memory. The .to_pandas_on_spark() method and the ps.DataFrame() constructor are both designed for this purpose, making them the correct choices here. Methods like .to_pandas() should be avoided for large datasets as they collect all data into memory, which can lead to performance and memory issues.

  • A. Incorrect.

    The .to_pandas() method converts a PySpark DataFrame to a Pandas DataFrame, which fully collects the data into the driver’s memory. This is not suitable for large datasets as it can cause memory issues.

  • B. Incorrect.

    The ps.from_pandas() function is used to convert a Pandas DataFrame to a Pandas on Spark DataFrame, not a PySpark DataFrame. Therefore, it cannot be used in this scenario.

  • C. Correct.

    The ps.DataFrame() constructor can be used to convert a PySpark DataFrame to a Pandas on Spark DataFrame without bringing all the data into the driver’s memory. This is a valid approach for large datasets.

  • D. Correct.

    The .to_pandas_on_spark() method is specifically designed to convert a PySpark DataFrame to a Pandas on Spark DataFrame while avoiding memory issues with large datasets. This is a valid method for the task.

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