Sales Data Cleaning Quiz for Analytics

1. A transaction date field shows impossible values like '2025-13-45'. What type of data quality check would catch this?

Format validation

Range validation

Referential integrity check

Uniqueness constraint

2. A sales dataset includes a 'Region' field with typos: 'North West', 'NorthWest', 'NW'. The best approach to resolve this is using a ____.

VLOOKUP or reference table for standardization

Manual find-and-replace for each variation

SUBSTITUTE() function on all three variations

Filtering to hide incorrect entries

3. Before merging sales data from multiple sources, what is the primary concern to address?

Ensuring all sources have identical column names

Verifying data quality and consistency across sources

Converting to the same file format

Removing all duplicate columns

4. Which scenario represents a referential integrity issue in a sales dataset?

A customer ID in sales records doesn't exist in the customer table

Two identical product names with different IDs

Missing values in an optional discount column

Sale amounts formatted inconsistently

5. A sales representative field shows names like 'John-Smith' and 'john smith'. To standardize, you would apply ____.

UPPER() and SUBSTITUTE() functions

TRIM() and LOWER() functions

CONCATENATE() and REPLACE() functions

FIND() and MID() functions

6. When performing a data quality assessment, what does 'completeness' measure?

The absence of duplicate records

The percentage of non-null values

The accuracy of data entries

The consistency of data formats

7. Your sales dataset contains a 'Discount' column with values ranging from -10% to 150%. Which values likely represent data entry errors?

Negative percentages only

Values above 100% only

Both negative and above 100%

Cannot determine without business context

8. Which of the following is a best practice when documenting data cleaning decisions?

Keep notes informally in emails

Create a data cleaning log with rules and rationale

Rely on memory of what was changed

Skip documentation if changes are obvious

9. A sales dataset has a customer ID column where some entries are text ('C001') and others are numeric (1). How should you handle this?

Leave as-is; both represent the same data

Standardize to a consistent data type

Create separate columns for each type

Delete the numeric entries

10. When should data cleaning typically occur in an analytics workflow?

After creating visualizations

During the data preparation phase

Only if errors are discovered later

After generating final reports

11. A sales dataset contains duplicate records where the same transaction ID appears twice. Which technique is most appropriate to remove these duplicates?

Use a DISTINCT or GROUP BY function

Manually delete rows one by one

Apply a filter to hide duplicates

Create a new column with unique identifiers

12. Your sales dataset has a 'State' column with values like 'CA', 'ca', 'California', 'CA '. This inconsistency should be resolved by ____.

Standardizing to a single format

Using a reference lookup table

Applying case conversion functions

All of the above

13. Which data profiling technique best identifies the range, distribution, and frequency of values in a sales amount column?

Descriptive statistics and histograms

Manual row-by-row review

Sorting by ascending order

Creating pivot tables only

14. A dataset contains sales data from three regions with different currency codes. Before merging, you should ____.

Combine them as-is

Standardize currency to one format

Remove regional identifiers

Create separate datasets

15. When validating a sales amount field, which rule is most appropriate?

All values must be positive numbers

Values must fall within business-defined ranges

No null values allowed

All of the above

16. Your dataset shows a customer's annual revenue as 5,000,000 while similar entries average 50,000. This is an example of an ____.

Outlier

Anomaly

Both a and b

Data validation error

17. A sales representative's name field contains extra spaces: ' John Smith '. Which function removes leading and trailing whitespace?

CLEAN()

TRIM()

SUBSTITUTE()

CONCATENATE()

18. When standardizing date formats in a sales dataset, which format is most universally compatible?

MM/DD/YYYY

DD-MM-YYYY

YYYY-MM-DD

Month DD, YYYY

19. A column in your dataset has 15% missing values. Which approach is most appropriate for handling this gap?

Delete the entire column

Impute using mean, median, or domain logic

Ignore missing values during analysis

Replace all with zero

20. Your sales data shows product prices in multiple formats (e.g., '$100', '100.00', '100'). What is this data quality issue called?

Inconsistent formatting

Missing data

Duplicate records

Outlier detection

Data Analyst Cleaning a Messy Sales Dataset Quiz