AI & Technical question

Design an in-memory cache system.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests systems design fundamentals: eviction policy, consistency trade-offs, and how you'd measure cache effectiveness.

Clarify use case and scale: a read-heavy lookup cache, confirm expected QPS and data size.
Choose the data structure: a hash map for O(1) lookup, backed by a doubly linked list for LRU eviction.
Decide the eviction policy: LRU by default, note LFU if access is skewed toward a few hot keys.
Address consistency: a TTL plus write-through or write-invalidate so the cache doesn't serve stale data.
Address scale: sharding across cache nodes with consistent hashing if one node isn't enough.
Define measurement: cache hit rate and p99 latency, with a target hit rate stated as an assumption.

Picks a specific eviction policy and justifies it against the stated access pattern.
Explicitly addresses cache invalidation and staleness, the hardest real part of cache design.
Names hit rate and p99 latency as concrete success metrics, with an assumption-labeled target.

Chapters of the AI PM course that teach what this question tests.