Autoscaling Cold Starts on Vertex AI

Quick Answer

The answer is to set a higher min replicas to maintain a baseline of warm instances. This is correct because autoscaling cold start solutions on Vertex AI hinge on the fact that new instances require several minutes to initialize and load the model, creating latency during traffic spikes. By raising the minimum number of replicas, you ensure a pool of pre-warmed instances is always ready to serve requests instantly, absorbing the initial surge while new instances spin up in the background. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI Prediction’s scaling behavior and the trade-off between cost and latency. A common trap is to assume that reducing the scaling window or enabling faster provisioning solves the cold start problem, but those options do not eliminate the inherent initialization delay. Remember the memory tip: “Warm the pool, don’t cool the spike”—keeping a baseline of warm instances is the only direct way to prevent latency from cold starts.