Problem
Scenario
You're working on a data pipeline where source data can arrive with missing fields, duplicate records, or conflicting values across systems. You want a practical approach for keeping downstream reporting usable while preserving traceability back to the raw inputs.
Question
How would you handle a dataset that is incomplete or inconsistent?
What This Tests
- Data quality controls for missing and inconsistent records
- ETL design for validation, quarantine, and remediation
- Idempotent loading to avoid duplicate downstream data
- Backfill and replay strategy after source corrections
Practicing as: Data Scientist interview at FIRSTNET GLOBALHi, I'll play your FIRSTNET GLOBAL interviewer for the Data Scientist role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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