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Core
Partitioning and Clustering
Partitioning splits one large table into physically separate chunks by a key (usually date), so a query with a matching filter reads only the relevant partitions instead of the whole table. Clustering and sort keys order data within storage so related rows sit together, improving locality and letting the engine skip blocks. Applied-AI interviews probe this because in a cloud warehouse you pay per byte scanned, and turning a full scan into a thin slice is the difference between a query that costs cents and one that costs dollars and minutes.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
SQL & Data EngineeringHow do you choose a partitioning and clustering strategy for a large analytics table?→SQL & Data EngineeringA Spark job that used to finish in minutes now takes hours. How do you diagnose and fix it?→SQL & Data EngineeringHow do partitioning, file formats (Parquet), and file layout affect query performance in a lakehouse?→SQL & Data EngineeringYou shipped a logic bug three months ago. How do you safely backfill and reprocess the affected data?→SQL & Data EngineeringImplement Slowly Changing Dimension Type 2 history tracking in a Delta lakehouse.→Coding & DSAImplement k-means from scratch, including k-means++ initialization and a convergence check.→
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