DMC Data Collection & Preparation 2

Question 1

A dataset's 'income' attribute is missing more often for high earners who decline to answer. What type of missingness is this?

Accepted Answer

Missing not at random (MNAR)

Answer

When the probability of missingness depends on the unobserved value itself, the data are missing not at random.

Question 2

Which imputation method best preserves relationships between variables when filling missing values?

Accepted Answer

Multiple imputation using predictive models

Answer

Multiple imputation models each missing value from other attributes and reflects uncertainty across several imputed datasets.

Question 3

An analyst rescales 'age' to the range [0,1] using (x − min)/(max − min). What technique is this?

Accepted Answer

Min-max normalization

Answer

Min-max normalization linearly maps values into a fixed range, typically [0,1].

Question 4

Why is min-max normalization sensitive to outliers?

Accepted Answer

A single extreme value stretches the range and compresses all other values

Answer

Because min and max define the scale, one extreme point squeezes the remaining values into a narrow interval.

Question 5

A retailer merges two customer tables where one stores 'CustID' and the other 'Customer_Number' for the same field. Which data integration problem is this?

Accepted Answer

Entity identification problem

Answer

The entity identification problem involves matching equivalent attributes or entities across different sources.

DMC - Data Mining Certification Practice Test

DMC - Data Mining Certification Practice Test

DMC Data Collection & Preparation 2