A data scientist is evaluating the impact of imputation on a numeric feature with a normal distribution. They notice that after mean imputation, the feature's variance is lower than the original. What is the primary reason for this?

The sample size increases, reducing variance.

Imputed values are all equal to the mean, reducing the spread of the data.

The mean is lower than the median, causing a shift.

Imputation removes outliers, reducing variance.

How can mean imputation affect the performance of a downstream linear regression model?

It can reduce the model's ability to detect relationships because variance is underestimated

It always improves model performance by increasing the sample size

It has no effect on model performance because the mean is a neutral value

It always worsens model performance because it introduces outliers

A data scientist is comparing mean and median imputation for a numeric feature that is normally distributed. They are concerned about the impact on the feature's variance. Which statement is correct?

Mean imputation reduces the variance more than median imputation.

Median imputation reduces the variance more than mean imputation.

Neither method affects the variance.

Both methods reduce the variance equally for a symmetric distribution.

DatabricksCertified Machine Learning Associate

Domain 2Objective 5

Compare and Contrast Imputing Missing Values with the Mean or Median or Mode Value MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Part of the Section 2: Data Processing domain, which makes up ~27% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~9–16 in this domain), expect 1–2 from this objective — we provide 14 practice questions to prepare you well beyond it. (estimate)

14questions here

3free pages

5concepts

Questions 11–14

11application · medium
A data scientist is evaluating the impact of imputation on a numeric feature with a normal distribution. They notice that after mean imputation, the feature's variance is lower than the original. What is the primary reason for this?
Select an answer first
12foundation · easy
How can mean imputation affect the performance of a downstream linear regression model?
Select an answer first
13application · medium
A data analyst is cleaning a dataset of sensor readings from a manufacturing process. The feature 'temperature' is normally distributed with no significant outliers, and the missing values appear to be randomly scattered. The analyst wants a simple imputation that minimizes distortion of the feature's variance. Which approach should they choose?
Select an answer first
14application · medium
A data scientist is comparing mean and median imputation for a numeric feature that is normally distributed. They are concerned about the impact on the feature's variance. Which statement is correct?
Select an answer first

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Compare and Contrast Imputing Missing Values with the Mean or Median or Mode Value MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 3)

Questions 11–14

A data scientist is evaluating the impact of imputation on a numeric feature with a normal distribution. They notice that after mean imputation, the feature's variance is lower than the original. What is the primary reason for this?

How can mean imputation affect the performance of a downstream linear regression model?

A data scientist is comparing mean and median imputation for a numeric feature that is normally distributed. They are concerned about the impact on the feature's variance. Which statement is correct?

Keep studying — Section 2: Data Processing