Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 373 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 373

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You are working on a machine learning task where you need to train different regression models for multiple customer groups based on their region. Each region's data is stored in a single dataframe. You want to use the Pandas Function API to group the data by region, train a unique model for each group, and store the predictions in the original dataframe. Which of the following steps are required to accomplish this task?

  1. A

    Use the groupby method to split the dataframe into groups based on the region column.

  2. B

    Define a custom function to train a model and generate predictions for each group.

  3. C

    Apply the custom function to each group using the apply method on the grouped dataframe.

  4. D

    Use the PySpark groupBy method instead of Pandas for better scalability.

  5. E

    Manually iterate over each group using a for loop to train models and generate predictions.

Show answer and explanation

Correct answers: A, B, C

Explanation

To train group-specific models using the Pandas Function API, you need to group the dataframe by a specific column (e.g., region) using the groupby method. Then, you define a custom function that encapsulates the training and prediction logic for each group. Finally, you use the apply method to apply the custom function to each group. This approach is efficient and leverages the capabilities of the Pandas Function API.

  • A. Correct.

    Correct. The groupby method is essential for splitting the dataframe into groups based on the region column, so you can apply group-specific operations.

  • B. Correct.

    Correct. A custom function is required to encapsulate the logic for training a model and generating predictions for each group.

  • C. Correct.

    Correct. The apply method is used to apply the custom function to each group, enabling group-specific computations.

  • D. Incorrect.

    Incorrect. While PySpark's groupBy method is useful for large-scale distributed data processing, the question specifically focuses on using Pandas and the Pandas Function API.

  • E. Incorrect.

    Incorrect. Manually iterating over groups is not needed with the Pandas Function API, as the apply method automates this process efficiently.

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