Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 374 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 374

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A retail company is analyzing sales data and wants to train group-specific machine learning models for each store to predict monthly sales. The dataset is stored as a Spark DataFrame and contains columns: 'store_id', 'month', 'sales', and 'features'. Which of the following methods should be used to train and apply group-specific models efficiently?

  1. A

    Use the Pandas Function API by defining a function to train a model for each group and applying it with groupby on 'store_id'.

  2. B

    Use Spark's built-in MLlib algorithm to train a single global model instead of group-specific models.

  3. C

    Convert the Spark DataFrame to a Pandas DataFrame, and manually loop through each unique 'store_id' to train a model for each group.

  4. D

    Use the Pandas Function API with groupby on 'store_id' and pass a UDF to train the models for each group.

  5. E

    Leverage the Pandas Function API to create a grouped map Pandas UDF for efficient group-specific model training and application.

Show answer and explanation

Correct answers: A, E

Explanation

The Pandas Function API is an efficient and scalable method to handle group-specific tasks in distributed environments like Spark. By using a grouped map Pandas UDF, you can define custom logic to train and apply machine learning models for each group, such as 'store_id', while leveraging Spark's distributed processing. This avoids the inefficiencies of manual loops or global models, making it the best approach for the given scenario.

  • A. Correct.

    Correct: The Pandas Function API allows you to define custom Python logic for grouped data processing, making it ideal for training group-specific models by leveraging Spark's distributed computation.

  • B. Incorrect.

    Incorrect: Training a single global model does not fulfill the requirement to create group-specific models for each store. This approach is not suitable for the scenario.

  • C. Incorrect.

    Incorrect: While it is possible to manually loop through each group in a Pandas DataFrame, this approach is inefficient and does not leverage Spark's distributed processing capabilities.

  • D. Incorrect.

    Incorrect: Using a UDF for this task is not recommended, as UDFs are not designed for group-specific processing and are less efficient compared to the Pandas Function API.

  • E. Correct.

    Correct: The Pandas Function API's grouped map Pandas UDF is specifically designed for distributed group-specific processing, enabling efficient training and application of models for each group.

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