Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 297 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 297

Select 3

You are tasked with building a machine learning pipeline in Spark ML to preprocess data and train a model. Which of the following statements correctly describe the role of an Estimator and a Transformer in this process?

  1. A

    An Estimator represents a machine learning algorithm or a stage that can fit data to produce a Transformer.

  2. B

    A Transformer takes a DataFrame as input and produces another DataFrame as output by applying some transformation.

  3. C

    An Estimator directly modifies the input DataFrame to output predictions or transformed features.

  4. D

    A Transformer always requires a training dataset to build its transformation logic.

  5. E

    An Estimator is used during the training phase, while a Transformer is used during the transformation or prediction phase.

Show answer and explanation

Correct answers: A, B, E

Explanation

In Spark ML, an Estimator is a machine learning abstraction used to fit data and produce a Transformer. A Transformer, on the other hand, applies transformations or predictions to a DataFrame based on the learned logic. Estimators are typically used during training, while Transformers are used for transformations or predictions.

  • A. Correct.

    Correct. An Estimator in Spark ML represents a machine learning algorithm or pipeline stage that can fit on a DataFrame to produce a Transformer.

  • B. Correct.

    Correct. A Transformer in Spark ML takes a DataFrame as input and applies the transformation defined by its logic to produce another DataFrame.

  • C. Incorrect.

    Incorrect. An Estimator does not directly modify the input DataFrame, it first fits the data to produce a Transformer, which can then be used to modify the DataFrame.

  • D. Incorrect.

    Incorrect. A Transformer does not require a training dataset, as it uses the transformation logic already defined (often learned during the Estimator's fit phase).

  • E. Correct.

    Correct. An Estimator is used during the training phase to learn from data, while a Transformer is applied during the transformation or prediction phase.

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