Data Cleaning Quiz: Can You Fix Dirty Data?

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3. Which metric is commonly used to measure data quality improvements after cleansing?

Completeness and accuracy percentages

Storage size reduction

Query execution time

User login frequency

4. Cross-field validation checks the consistency and relationships between multiple data fields.

True

False

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6. Which scenario best demonstrates the need for data cleansing in a real-world context?

Customer database with duplicate entries and inconsistent address formats

Newly created empty database

Read-only archived data

Encrypted backup files

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8. What does whitespace trimming accomplish in data cleansing?

Removes leading and trailing spaces from text fields

Deletes entire columns

Encrypts text data

Converts text to numbers

9. Data cleansing is a one-time process that should be performed once before analysis.

True

False

10. Which tool or technique helps visualize data distribution and identify anomalies?

Histograms and box plots

Encryption algorithms

Backup utilities

Compression software

11. What is the primary goal of data cleansing in enterprise environments?

Ensure data accuracy, completeness, and consistency for reliable analysis

Reduce storage costs by deleting unnecessary records

Increase database processing speed

Encrypt sensitive data before analysis

12. Data validation rules are typically established during which phase of data cleansing?

Post-analysis reporting

Planning and assessment phase

Data deletion

Archiving

13. Which of the following represents a valid approach to handling inconsistent categorical data?

Standardizing case and spelling variations

Randomly assigning values

Deleting all categorical fields

Converting to numeric codes without mapping

14. Outliers in data should always be removed immediately.

True

False

15. What is data profiling used for in cleansing workflows?

Encrypting sensitive information

Analyzing data structure and identifying quality issues

Compressing large datasets

Creating backup copies

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17. Which data validation rule ensures values fall within an acceptable range?

Pattern matching

Range validation

Format validation

Referential integrity

18. When handling missing values in a dataset, which approach is generally least recommended?

Deleting rows with missing values

Imputing with mean or median values

Using predictive modeling to estimate missing data

Marking missing values with a placeholder flag

19. What technique is most effective for identifying and removing duplicate records?

Manual inspection of each row

Using hash functions and deduplication algorithms

Randomly sampling 10% of data

Sorting data alphabetically

20. Which of the following is NOT a common data quality issue?

Duplicate records

Missing values

Standardized formatting

Inconsistent data types

Data Cleaning Quiz: Can You Fix Dirty Data?

1. Referential integrity ensures that foreign keys reference valid ____ in related tables.

2.

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

2. Documentation of data cleansing processes is important for maintaining ____ and reproducibility.

3. Which metric is commonly used to measure data quality improvements after cleansing?

4. Cross-field validation checks the consistency and relationships between multiple data fields.

5. Type coercion in data cleansing refers to converting data from one ____ to another.

6. Which scenario best demonstrates the need for data cleansing in a real-world context?

7. A ____ is a comprehensive examination of data structure, content, and relationships.

8. What does whitespace trimming accomplish in data cleansing?

9. Data cleansing is a one-time process that should be performed once before analysis.

10. Which tool or technique helps visualize data distribution and identify anomalies?

11. What is the primary goal of data cleansing in enterprise environments?

12. Data validation rules are typically established during which phase of data cleansing?

13. Which of the following represents a valid approach to handling inconsistent categorical data?

14. Outliers in data should always be removed immediately.

15. What is data profiling used for in cleansing workflows?

16. Data standardization involves converting data into a consistent ____.

17. Which data validation rule ensures values fall within an acceptable range?

18. When handling missing values in a dataset, which approach is generally least recommended?

19. What technique is most effective for identifying and removing duplicate records?

20. Which of the following is NOT a common data quality issue?