You are an ML engineer at a retail company that uses a SageMaker model to generate product recommendations for customers in real-time. During peak shopping periods, the traffic to the recommendation engine increases dramatically. The company needs to ensure that the model endpoint can handle these spikes in demand without compromising on response time or customer experience. At the same time, you want to optimize costs by scaling down resources during periods of low demand. You are evaluating different scaling policies to manage this dynamic workload effectively. Which scaling policy is the MOST SUITABLE for this scenario, and why?