Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 352 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 352

Select 3

You are working on a Databricks notebook and want to use the Pandas on Spark API to process a large dataset that doesn't fit into memory. Which of the following steps are necessary to correctly import and use the Pandas on Spark API?

  1. A

    Import the Pandas on Spark API using import pyspark.pandas as ps.

  2. B

    Install the Pandas on Spark library using pip install pandas_on_spark in your Databricks notebook.

  3. C

    Use the Pandas on Spark API by calling ps.DataFrame() to create a DataFrame.

  4. D

    Convert an existing PySpark DataFrame to a Pandas on Spark DataFrame using ps.from_pandas().

  5. E

    Use the Pandas on Spark API to leverage distributed computations while maintaining a pandas-like syntax.

Show answer and explanation

Correct answers: A, C, E

Explanation

To use Pandas on Spark in Databricks, you need to import it using import pyspark.pandas as ps. This API allows for distributed computations while maintaining a pandas-like syntax. You do not need to install any additional libraries because Pandas on Spark is included with PySpark in Databricks. Additionally, you can create a DataFrame using ps.DataFrame() to leverage the functionality of the API.

  • A. Correct.

    Correct. The Pandas on Spark API is imported using import pyspark.pandas as ps in Databricks, which allows you to use its functionality.

  • B. Incorrect.

    Incorrect. Pandas on Spark is included as part of the PySpark library in Databricks, so no additional library installation is required.

  • C. Correct.

    Correct. The Pandas on Spark API provides a ps.DataFrame() method to create a distributed DataFrame similar to pandas' syntax.

  • D. Incorrect.

    Incorrect. The ps.from_pandas() method is used to convert a pandas DataFrame to a Pandas on Spark DataFrame, not a PySpark DataFrame.

  • E. Correct.

    Correct. Pandas on Spark allows users to perform distributed computations with a pandas-like syntax, making it suitable for large datasets.

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