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

Forward fill and interpolation preserve temporal continuity, making them ideal for time series data where adjacent values are related.

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

Outliers may represent genuine rare events, and removing them without understanding the domain can eliminate important signal from the data.

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

One-hot encoding a high-cardinality feature creates 50 new binary columns, dramatically increasing dimensionality and sparsity.

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

Data leakage occurs when information that would not be available at prediction time is used during training, leading to overly optimistic performance estimates.

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

Robust scaling uses the median and IQR, which are resistant to outliers, unlike min-max or z-score methods that are heavily influenced by extreme values.

Data Science Practice Test

Data Science Practice Test

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