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Microsoft

DP-100 · Question #27

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. The requirement explicitly states that the operation must not affect the dimensionality of the feature set. Dimensionality refers to the number of features (columns). Removing an entire column reduces the number of features, directly changing the dimensionality. This violates…

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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 analyzing a numerical dataset which contains missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set. You need to analyze a full dataset to include all values. Solution: Remove the entire column that contains the missing data point. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(47 responses)
  • A
    9% (4)
  • B
    91% (43)

Explanation

The requirement explicitly states that the operation must not affect the dimensionality of the feature set. Dimensionality refers to the number of features (columns). Removing an entire column reduces the number of features, directly changing the dimensionality. This violates the stated constraint. Even if the column had only a few missing values, deleting it removes all valid data in that column as well, resulting in information loss and a smaller feature space.

Topics

#Data cleaning#Missing values#Dimensionality#Data preprocessing

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