Which imputation method is most robust to outliers when estimating the central value of a numerical feature?

All imputation methods are equally robust to outliers

How does mean imputation typically affect the variance of a feature compared to the original (pre-missingness) distribution?

It increases the variance because the imputed values are randomly distributed around the mean

It reduces the variance because imputed values are all equal to the mean

It has no effect on variance because the mean is a measure of central tendency

It increases the variance because the mean is an extreme value

A data team is comparing imputation methods for a numeric feature with a symmetric distribution and no outliers. They want to minimize the bias introduced by imputation. Which statement is correct?

Mode imputation is the best choice to minimize bias for numeric data.

Median imputation introduces less bias than mean imputation for symmetric distributions.

Mean imputation introduces less bias than median imputation for symmetric distributions.

Both mean and median imputation introduce the same bias for symmetric distributions.

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 1)

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 1–5

1application · medium
A data scientist is preparing a dataset with a numeric feature representing household income. The distribution is heavily right-skewed with a few very high-income outliers. The feature has about 8% missing values, and the team plans to use a linear regression model. Which imputation approach is most appropriate?
Select an answer first
2foundation · easy
Which imputation method is most robust to outliers when estimating the central value of a numerical feature?
Select an answer first
3foundation · easy
How does mean imputation typically affect the variance of a feature compared to the original (pre-missingness) distribution?
Select an answer first
4application · medium
A data team is comparing imputation methods for a numeric feature with a symmetric distribution and no outliers. They want to minimize the bias introduced by imputation. Which statement is correct?
Select an answer first
5application · medium
A data scientist is building a logistic regression model to predict customer default. One predictor, 'credit_score', has a left-skewed distribution with a long tail of low scores. The missing rate is 10%. Which imputation method is likely to have the least adverse impact on the model's calibration?
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 1)

Questions 1–5

Which imputation method is most robust to outliers when estimating the central value of a numerical feature?

How does mean imputation typically affect the variance of a feature compared to the original (pre-missingness) distribution?

A data team is comparing imputation methods for a numeric feature with a symmetric distribution and no outliers. They want to minimize the bias introduced by imputation. Which statement is correct?

A data scientist is building a logistic regression model to predict customer default. One predictor, 'credit_score', has a left-skewed distribution with a long tail of low scores. The missing rate is 10%. Which imputation method is likely to have the least adverse impact on the model's calibration?

Keep studying — Section 2: Data Processing