MS-DS Master of Data science FREE MS-DS Master of Data science Data Wrangling and Preprocessing Questions and Answers 3

Question 1

What is the key difference between StandardScaler and RobustScaler in scikit-learn?

Accepted Answer

RobustScaler uses median and IQR making it less sensitive to outliers

Answer

StandardScaler is designed specifically for sparse data

Answer

RobustScaler always produces values between 0 and 1

Answer

StandardScaler cannot handle negative values

Question 2

Which technique is used to detect and handle duplicate records that are not exact matches but refer to the same entity?

Accepted Answer

Record linkage or fuzzy matching

Answer

Primary key constraints

Answer

Data type casting

Answer

Pivot table aggregation

Question 3

When applying a pipeline in scikit-learn, why should you fit the preprocessor only on training data and then transform both training and test data?

Accepted Answer

To prevent data leakage from the test set into the model

Answer

To speed up the transformation process

Answer

To ensure the test set has different scaling parameters

Answer

To reduce memory usage during preprocessing

Question 4

What problem does the Synthetic Minority Over-sampling Technique (SMOTE) address in data preprocessing?

Accepted Answer

Class imbalance by generating synthetic minority class samples

Answer

Missing values by interpolating between existing observations

Answer

High cardinality by grouping rare categories

Answer

Multicollinearity by creating orthogonal features

Question 5

Which pandas method would you use to apply different aggregation functions to different columns after a groupby operation?

Accepted Answer

agg() with a dictionary mapping columns to functions

Answer

transform() with a lambda function

Answer

apply() with a single function

Answer

filter() with a boolean condition

Question 6

What is the purpose of using regular expressions in data wrangling?

Accepted Answer

To define complex text patterns for extracting, matching, or replacing string data

Answer

To perform numerical calculations on DataFrame columns

Answer

To join multiple tables on composite keys

Answer

To optimize memory usage of string columns