Your question is Missing and Noisy Data Handling. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you handle missing or noisy data in a production pipeline?
Explain how you would distinguish expected missing values from data defects, validate records at ingestion, and prevent corrupt data from reaching downstream consumers. Cover batch and streaming considerations, quarantine or dead-letter handling, schema enforcement, imputation or rejection policies, replayability, and monitoring. Include how you would preserve lineage, alert on changing data-quality patterns, and backfill corrected records without creating duplicates. Discuss trade-offs between failing the pipeline, skipping records, and publishing partially complete data.