Which solution will meet these requirements?

Explanations:

A predictive scaling policy forecasts future demand based on historical data, but it may not address immediate spikes in demand. Setting the default instance warmup time to 0 seconds is not advisable since instances need time to become fully operational after launch. An instance maintenance policy does not effectively handle the user data script during scaling.

A dynamic scaling policy adjusts capacity based on real-time demand, which is suitable for handling immediate spikes. However, setting the default instance warmup time to 0 seconds is still not ideal, as instances require time to initialize and run the user data script effectively. Lifecycle hooks are useful for managing the lifecycle of instances but do not ensure faster readiness after scaling.

A predictive scaling policy may not react quickly enough to real-time traffic spikes. While enabling warm pools can allow pre-warmed instances to be available more quickly, running the user data script as part of an instance maintenance policy is not effective for newly launched instances since it cannot ensure they are ready in time to handle traffic.

A dynamic scaling policy is the best fit for responding to immediate demand changes, while enabling warm pools allows pre-warmed instances to be available quickly. Using lifecycle hooks ensures that the user data script runs as needed during the scaling process, making instances ready to handle traffic more efficiently and reducing latency.