PL-300 · 1.2.2 · Prepare the data
Fix data quality issues
Resolve inconsistencies, unexpected or null values, and data quality issues
Match each quality problem to the right cleanup operation.
Let the profile expose the problem
Profiling can reveal errors, empty values, repeated values, and suspicious distributions before you change anything.
Match symptom to cleanup
| Symptom | Typical action |
|---|---|
| Missing values | Fill or replace where business meaning allows |
| Inconsistent labels | Replace Values, trim, clean, or normalize case |
| Duplicate business rows | Normalize comparison fields, then remove duplicates |
| Error values | Inspect, replace, remove, or isolate the errors |
Remove Duplicates uses the selected columns and is , so normalize casing before deduplication when case should not create separate values.
Exam clue: identify the symptom first, then choose the narrow transformation that fixes it.
Key points
- Fill: replaces from nearby values
- Replace Values: fixes inconsistent cell content
- Duplicates: comparison is
- Normalize case: do this before deduplication
Click an underlined term for a one-line explanation, or see them all in the glossary .
Exam trap
Remove Duplicates treats Oslo and OSLO as different values unless you normalize casing first.
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