nerdexam
Microsoft

DP-100 · Question #372

You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features…

The correct answer is C. FeaturizationMode. The FeaturizationMode enum from the Azure Machine Learning automl package controls how automated machine learning handles data preprocessing steps, specifically whether it automatically imputes missing values and encodes categorical features. It has three modes: 'Auto'…

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Question

You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features with few distinct values. You need to control whether automated machine learning automatically imputes missing values and encode categorical features as part of the training task. Which enum of the automl package should you use?

Options

  • AForecastHorizonMode
  • BRegressionModels
  • CFeaturizationMode
  • DRegressionPrimaryMetrics

How the community answered

(26 responses)
  • B
    4% (1)
  • C
    88% (23)
  • D
    8% (2)

Explanation

The FeaturizationMode enum from the Azure Machine Learning automl package controls how automated machine learning handles data preprocessing steps, specifically whether it automatically imputes missing values and encodes categorical features. It has three modes: 'Auto' (default, performs featurization automatically), 'Custom' (allows user-defined featurization steps), and 'Off' (disables all featurization). The other options are unrelated to featurization control: ForecastHorizonMode controls how the forecasting horizon is determined, RegressionModels is an enum listing available regression algorithms to try, and RegressionPrimaryMetrics defines the metric used to evaluate and rank regression models during training.

Topics

#Azure Machine Learning AutoML#Feature Engineering#Data Preprocessing#Python SDK v2

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