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Microsoft

AI-900 · Question #154

Drag and Drop Question You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? To answer…

The correct answer is Select Columns in Dataset; Split Data; Linear Regression. To predict automobile prices in Azure ML Designer, you first use 'Select Columns in Dataset' to choose relevant features and remove unnecessary columns, then 'Split Data' to divide the dataset into training and testing sets, and finally 'Linear Regression' as the algorithm…

Submitted by amina.ke· Mar 30, 2026Design and prepare a machine learning solution - specifically building and training a regression model using Azure Machine Learning Designer (DP-100 / AI-900)

Question

Drag and Drop Question You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Answer:

Exhibit

AI-900 question #154 exhibit

Answer Area

Drag items

Convert to CSVK-Means ClusteringLinear RegressionSplit DataSelect Columns in DatasetSummarize Data

Correct arrangement

  • Select Columns in Dataset
  • Split Data
  • Linear Regression

Explanation

To predict automobile prices in Azure ML Designer, you first use 'Select Columns in Dataset' to choose relevant features and remove unnecessary columns, then 'Split Data' to divide the dataset into training and testing sets, and finally 'Linear Regression' as the algorithm because price prediction is a continuous numerical output (regression problem). This follows the standard supervised learning pipeline: feature selection → data splitting → model training with an appropriate regression algorithm.

Topics

#Azure Machine Learning Designer#Regression#Supervised Learning#ML Pipeline

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