Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 637 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 637

Select 3

You are working on a machine learning project in Databricks. You have a trained scikit-learn model and a Pandas DataFrame named 'new_data' containing new instances for which predictions are required. Which of the following steps would allow you to perform batch inference using the model and the DataFrame efficiently?

  1. A

    Use the model's predict() method on the DataFrame directly to generate predictions.

  2. B

    Ensure that the feature columns in 'new_data' match the feature names used during training before calling the predict() method.

  3. C

    Convert the Pandas DataFrame to a NumPy array before passing it to the model's predict() method.

  4. D

    Use Spark DataFrame's .toPandas() method to convert 'new_data' to a Pandas DataFrame before calling predict().

  5. E

    Verify that there are no missing values in 'new_data' before performing batch inference.

Show answer and explanation

Correct answers: B, C, E

Explanation

To perform batch inference using a scikit-learn model with a Pandas DataFrame, it is critical to ensure the input data matches the training format, including feature names and data structure. Converting the DataFrame to a NumPy array is often required for compatibility, and addressing missing values ensures the process runs smoothly. These steps help avoid common issues during inference.

  • A. Incorrect.

    This option is incorrect because the model's predict() method cannot be directly applied to a Pandas DataFrame unless the DataFrame is preprocessed to match the format used during training.

  • B. Correct.

    This option is correct because ensuring that the feature columns in the DataFrame match the feature names used during training is a necessary step for the model to interpret the input correctly.

  • C. Correct.

    This option is correct because scikit-learn models often expect input data in the form of a NumPy array, so converting the Pandas DataFrame to a NumPy array is advisable.

  • D. Incorrect.

    This option is incorrect because converting the DataFrame to Pandas is unnecessary if 'new_data' is already a Pandas DataFrame.

  • E. Correct.

    This option is correct because missing values in the input DataFrame can cause errors or unexpected behavior during batch inference.

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