FREE Data Science Data Wrangling and Preprocessing Questions and Answers

Question 1

A dataset for customer analysis contains a 'Country' column with entries like "USA", "U.S.", "United States", and "America".
Before using this feature in a model, a data scientist combines these into a single category, "USA".
What specific data quality issue is being addressed?

Accepted Answer

Lack of consistency

Answer

Data volatility

Answer

Missing values

Answer

Incorrect data type

Question 2

A data scientist is preparing features for a machine learning model that is sensitive to feature scale. One feature has a distribution that is approximately Gaussian (normal), while another is heavily skewed. Which combination of scaling techniques is generally most appropriate?

Accepted Answer

Apply standardization (Z-score scaling) to the Gaussian feature and normalization (Min-Max scaling) to the skewed feature.

Answer

Apply log transformation to both features.

Answer

Apply normalization (Min-Max scaling) to the Gaussian feature and standardization to the skewed feature.

Answer

Apply standardization (Z-score scaling) to both features.

Question 3

You are analyzing a dataset of housing prices where the 'Square_Footage' feature contains several legitimate but extreme values that are significantly influencing the mean. To prepare this data for a linear regression model, which of the following is the most robust method to handle the influence of these outliers?

Accepted Answer

Capping the feature at the 1st and 99th percentile values.

Answer

Deleting all rows identified as outliers.

Answer

Imputing the outliers with the feature's mean.

Answer

Performing one-hot encoding on the feature.

Question 4

A data analyst has a dataset where each row represents a student, and columns represent their scores in different subjects: `StudentID`, `Math_Score`, `Science_Score`, `History_Score`. For a specific analysis, the data needs to be restructured so that each row contains a student ID, a subject, and the corresponding score. Which data wrangling operation should be performed?

Accepted Answer

Melting

Answer

Aggregating

Answer

Pivoting

Answer

Merging

Question 5

In a clinical trial dataset, data for a follow-up blood pressure measurement is more likely to be missing for patients whose initial blood pressure was very high, as they may have dropped out of the study due to health complications. This scenario is an example of which type of missing data mechanism?

Accepted Answer

Missing Not at Random (MNAR)

Answer

Missing Completely at Random (MCAR)

Answer

Structurally Missing Data

Answer

Missing at Random (MAR)

Question 6

A data scientist decides to convert a continuous 'Age' feature into a categorical 'Age_Group' feature (e.g., '18-25', '26-40', '41-60', '61+'). Which of the following is a primary benefit of this technique, known as binning?

Accepted Answer

It helps to capture non-linear relationships when using linear models.

Answer

It increases the precision of the data by adding more information.

Answer

It guarantees that the new feature will have a normal distribution.

Answer

It is the only way to handle missing values in the original continuous feature.

A dataset for customer analysis contains a 'Country' column with entries like "USA", "U.S.", "United States", and "America".Before using this feature in a model, a data scientist combines these into a single category, "USA".What specific data quality issue is being addressed?

A dataset for customer analysis contains a 'Country' column with entries like "USA", "U.S.", "United States", and "America".
Before using this feature in a model, a data scientist combines these into a single category, "USA".
What specific data quality issue is being addressed?