Your question is Real-Time Inference at Scale. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Describe the process of scaling model inference for a real-time API handling thousands of requests per second at RBC.
Explain how you would clarify the workload, estimate capacity, select serving infrastructure, and scale horizontally while meeting latency, availability, security, and cost requirements. Address online versus batch inference, deployment safety, observability, feature freshness, training-serving skew, and graceful degradation. State assumptions explicitly where the prompt does not provide workload details.
