Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 299 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 299

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You are tasked with building a machine learning pipeline in Databricks using Spark ML to predict customer churn. The dataset contains categorical and numerical features, and your pipeline must include the following steps: data preprocessing (handling missing values, encoding categorical variables, and scaling numerical features), a machine learning model, and evaluation of the model's performance. Which of the following steps are required to build such a pipeline?

  1. A

    Use VectorAssembler to combine feature columns into a single feature vector.

  2. B

    Use StringIndexer to encode categorical columns as numerical indices.

  3. C

    Use StandardScaler to standardize numerical columns.

  4. D

    Directly fit a decision tree model on the raw dataset without any preprocessing.

  5. E

    Add an evaluator like RegressionEvaluator or BinaryClassificationEvaluator to assess model performance.

Show answer and explanation

Correct answers: A, B, C, E

Explanation

To build a machine learning pipeline in Spark ML, you need to preprocess the data by encoding categorical variables, scaling numerical features, and assembling them into a feature vector. Then, you can fit a machine learning model and evaluate its performance using an evaluator. Skipping any of these steps, such as preprocessing or evaluation, would result in an incomplete or non-functional pipeline.

  • A. Correct.

    Correct: VectorAssembler is used to combine multiple feature columns into a single vector that Spark ML models can process. This is a required step for Spark ML pipelines.

  • B. Correct.

    Correct: StringIndexer is essential for converting categorical data into numerical representations, which are required by Spark ML models.

  • C. Correct.

    Correct: StandardScaler is commonly used to normalize numerical columns to ensure they have a mean of 0 and a standard deviation of 1, which improves model performance.

  • D. Incorrect.

    Incorrect: Directly fitting a model on raw data without preprocessing will not work in Spark ML pipelines as models require features to be in a vectorized format, and categorical data must be encoded first.

  • E. Correct.

    Correct: Adding an evaluator like RegressionEvaluator or BinaryClassificationEvaluator allows you to assess the model's performance, which is a critical step in the pipeline.

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