You optimize an AI inference API that uses Redis caching.

Detailed Explanation: If the priority is to minimize stale results, source-driven invalidation is the direct control: when the underlying record changes, delete the corresponding cache entry so the next request repopulates it from current data. Sliding expiration can keep a frequently accessed stale item alive, disabling expiration is worse, and an allkeys-LRU policy evicts according to memory pressure rather than data freshness. Invalidation therefore best addresses correctness without requiring constant cache-wide maintenance.

Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.

Official Microsoft Learn References: AI-200 Study Guide | Azure Managed Redis documentation