nerdexam
Microsoft

DP-100 · Question #75

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…

The correct answer is B. No. Quantiles normalization with QuantileIndex is a data normalization technique that rescales feature values so they follow a uniform or normal distribution-it does not produce binned (discretized) output. The goal requires grouping values into bins to predict a target column…

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Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are a data scientist using Azure Machine Learning Studio. You need to normalize values to produce an output column into bins to predict a target column. Solution: Apply a Quantiles normalization with a QuantileIndex normalization. Does the solution meet the GOAL?

Options

  • AYes
  • BNo

How the community answered

(46 responses)
  • A
    24% (11)
  • B
    76% (35)

Explanation

Quantiles normalization with QuantileIndex is a data normalization technique that rescales feature values so they follow a uniform or normal distribution-it does not produce binned (discretized) output. The goal requires grouping values into bins to predict a target column, which is a discretization/binning operation. For that purpose, the correct module in Azure ML Studio is 'Group Data into Bins' using modes such as Quantiles binning, Equal Width, Custom Edges, or MDL. Normalization and binning are distinct operations, so this solution does not meet the goal.

Topics

#Data Preprocessing#Normalization#Binning#Azure Machine Learning

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