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partitioning

AI, ML & GenAI interview questions tagged partitioning, across every topic.

5 questions · 2 unlocked for you

Concepts behind "partitioning"

The curriculum that explains the ideas these questions test.

Core
⚙️ System Design for AI in ProductionSign in
Consistent Hashing and ShardingSharding spreads data across nodes so no single machine holds everything, but naive modulo hashing remaps almost every key when a node joins or leaves. Consistent hashing places nodes and keys on a hash ring so that adding or removing a node only reshuffles the keys near it, roughly K/N keys instead of all of them. Virtual nodes even out load imbalance. AI, ML, and GenAI engineer interviews probe it because vector indexes, KV caches, and feature stores are all sharded, and rebalancing cost is the difference between a rolling deploy and an outage.
Core
🗄️ Data & SQL EngineeringSign in
Partitioning and ClusteringPartitioning 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 rather than the whole table. Clustering and sort keys order data within storage so related rows sit together, improving locality and letting the engine skip blocks. AI, ML, and GenAI interviews probe this because in a cloud warehouse you pay per byte scanned, and turning a full scan into a thin slice separates a query that costs cents from one that costs dollars and minutes.
Core
🗄️ Data & SQL EngineeringSign in
Backfills and ReprocessingA backfill recomputes historical data after a bug fix, a new column, or a logic change, and it is where fragile pipelines break. The safe pattern is partition-by-partition reprocessing with idempotent writes so reruns do not double-count, on isolated compute so production stays healthy, and validated against the old table before you swap. AI, ML, and GenAI interviews probe it because backfilling years of data without corrupting live tables or melting the warehouse separates engineers who have run production from those who have not.