For which type of data distribution is median imputation particularly recommended over mean imputation?

Symmetric distributions with no outliers

Skewed distributions with outliers

Categorical distributions with no natural order

Uniform distributions with a wide range

Why might mode imputation be a poor choice for a continuous numerical feature in a machine learning model?

The mode may not be representative and can introduce bias in the feature's distribution

The mode is always the largest value in the dataset

The mode is only defined for categorical data

The mode always increases the variance of the feature

A data scientist is working with a dataset where a numeric feature is normally distributed with no outliers. The missing rate is 5%. They are considering mean imputation. What is the primary advantage of mean imputation in this scenario?

It preserves the overall mean of the feature.

It preserves the variance of the feature.

It is the best choice for categorical data.

For which type of missing data is mode imputation most appropriate?

Ordinal features where the order matters but distances are unknown

Continuous numerical features with a skewed distribution

Categorical features such as product category or color

Continuous numerical features with a normal 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 2)

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 6–10

6foundation · easy
For which type of data distribution is median imputation particularly recommended over mean imputation?
Select an answer first
7foundation · easy
Why might mode imputation be a poor choice for a continuous numerical feature in a machine learning model?
Select an answer first
8application · medium
A machine learning engineer is working on a churn prediction model. The dataset includes a categorical feature 'customer_segment' with values 'Basic', 'Standard', and 'Premium'. About 5% of the records have this field missing. The class distribution is: Basic 70%, Standard 20%, Premium 10%. What is the most appropriate imputation strategy for this feature?
Select an answer first
9expert · medium
A data scientist is working with a dataset where a numeric feature is normally distributed with no outliers. The missing rate is 5%. They are considering mean imputation. What is the primary advantage of mean imputation in this scenario?
Select an answer first
10foundation · easy
For which type of missing data is mode imputation most appropriate?
Select an answer first

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