Data Quality: Missing and Duplicate Data Management

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2. Removing duplicates after data integration is considered a best practice in data preparation. True or False?

True

False

3. Which scenario represents Missing Completely At Random (MCAR)?

Missing salary values for high-income employees

Missing survey responses related to survey question difficulty

A random equipment malfunction causes missing sensor readings

Missing age values correlating with income level

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5. What is a primary advantage of multiple imputation over single imputation methods?

It eliminates all missing data instantly

It accounts for uncertainty by creating multiple complete datasets

It requires no computational resources

It prevents duplicate records entirely

6. MAR (Missing At Random) occurs when missing data depends on observed variables but not unobserved ones. True or False?

True

False

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8. Which of the following is NOT a valid approach for handling duplicate records?

Identifying duplicates using primary key columns

Using deduplication algorithms with exact matching

Ignoring duplicates during analysis

Merging duplicate records and aggregating values

9. Removing exact duplicate rows preserves data integrity while reducing redundancy. True or False?

True

False

10. Which technique uses statistical models to estimate missing values based on the relationship with other variables?

Simple deletion

Regression imputation

Mode imputation

Last observation carried forward

11. What is the primary consequence of not addressing missing data before analysis?

Increased storage requirements

Biased results and reduced statistical power

Slower data retrieval times

Improved data compression

12. Which imputation method is most appropriate when missing data depends on observed values?

Deletion

Mean imputation

Multiple imputation by chained equations (MICE)

Listwise deletion

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14. What is the primary drawback of deleting all rows with missing values?

It increases file size

It reduces dataset size and may cause information loss

It slows down computational processing

It introduces new missing values

15. Which of the following is a common source of duplicate data in data acquisition?

Data extracted from a single source system

Merging datasets from multiple systems without deduplication

Automated data validation rules

Encryption protocols during transfer

16. Duplicate records occur when the same observation appears more than once in a dataset. True or False?

True

False

17. Which imputation technique uses values from adjacent time periods in time-series data?

Hot deck imputation

Forward fill

Multiple imputation

Regression imputation

18. What does MCAR stand for in the context of missing data?

Missing Completely At Random

Missing Conditional Absence Record

Multiple Case Attribute Removal

Missing Correlated Attribute Ratio

19. Which method assumes missing data is randomly distributed across all observations?

Mean imputation

Forward fill

Complete case analysis

K-nearest neighbors imputation

Data+ Handling Missing and Duplicate Data Quiz

1. Missing data that is not random and depends on unobserved factors is classified as____.

2.

What first name or nickname would you like us to use?

2. Removing duplicates after data integration is considered a best practice in data preparation. True or False?

3. Which scenario represents Missing Completely At Random (MCAR)?

4. Duplicate detection using record linkage requires comparison of____across records.

5. What is a primary advantage of multiple imputation over single imputation methods?

6. MAR (Missing At Random) occurs when missing data depends on observed variables but not unobserved ones. True or False?

7. Hot deck imputation replaces missing values with values from____similar records.

8. Which of the following is NOT a valid approach for handling duplicate records?

9. Removing exact duplicate rows preserves data integrity while reducing redundancy. True or False?

10. Which technique uses statistical models to estimate missing values based on the relationship with other variables?

11. What is the primary consequence of not addressing missing data before analysis?

12. Which imputation method is most appropriate when missing data depends on observed values?

13. Fuzzy matching identifies potential duplicate records based on____similarity thresholds.

14. What is the primary drawback of deleting all rows with missing values?

15. Which of the following is a common source of duplicate data in data acquisition?

16. Duplicate records occur when the same observation appears more than once in a dataset. True or False?

17. Which imputation technique uses values from adjacent time periods in time-series data?

18. What does MCAR stand for in the context of missing data?

19. Which method assumes missing data is randomly distributed across all observations?