Data Science Data Science Data Cleaning and Preparation Questions and Answers 2

Question 1

Which technique is most appropriate for handling missing values in a time series dataset?

Accepted Answer

Forward fill or interpolation

Answer

Deleting all rows with missing values

Answer

Replacing with the global mean

Answer

Ignoring missing values entirely

Question 2

What is the primary risk of removing outliers without domain knowledge?

Accepted Answer

Losing valid but extreme observations that represent real phenomena

Answer

Improving model accuracy too much

Answer

Reducing dataset size below minimum thresholds

Answer

Creating new duplicate records

Question 3

When performing one-hot encoding on a categorical variable with 50 unique values, what problem is most likely to arise?

Accepted Answer

The curse of dimensionality from creating too many sparse features

Answer

Loss of ordinal relationships between categories

Answer

Automatic deletion of the original column

Answer

Negative values appearing in the encoded columns

Question 4

What does the term 'data leakage' refer to in the context of data preparation?

Accepted Answer

Information from outside the training set improperly influencing model building

Answer

Sensitive data being exposed to unauthorized users

Answer

Data being lost during transfer between systems

Answer

Memory overflow during large dataset processing

Question 5

Which method is best suited for normalizing a feature that contains significant outliers?

Accepted Answer

Robust scaling using median and interquartile range

Answer

Min-max scaling to the 0-1 range

Answer

Z-score standardization using mean and standard deviation

Answer

Log transformation followed by min-max scaling

Question 6

What is the correct order of operations when preparing a dataset with missing values and features requiring scaling?

Accepted Answer

Impute missing values first, then apply feature scaling

Answer

Apply feature scaling first, then impute missing values

Answer

Remove missing values and scaling simultaneously

Answer

Scale only the complete cases and leave missing values as-is