Systems Design Concepts Practice Test

The idea is to manage cache capacity using multiple, practical signals rather than a single metric. Scale when you’re genuinely approaching the limits or when demand is high, so you keep performance from degrading. If memory usage climbs above 80%, you’re near the memory cap and eviction pressure rises, so adding capacity helps. If the workload pushes throughput beyond a high threshold (like 100k ops/sec), the cache is under heavier use and may start showing latency or miss penalties unless you scale. If latency is already very good (below 0.5 ms), you have headroom to scale ahead of expected growth to preserve that fast response as traffic increases. Using these combined conditions helps ensure scaling happens in response to real pressure and also in anticipation of rising load, rather than based on a single metric that might mislead.