docs: append missing answers to fulfill all exam criteria

q4-scalability/calculations.md

@@ -22,3 +22,15 @@ To ensure high availability and responsiveness under a load of 15,000 requests p
 
 ## Conclusion
 To safely handle 15,000 req/s while maintaining a 30% safety margin (which helps absorb sudden traffic spikes or the failure of a few containers), the auto-scaling group should be configured to maintain a baseline of **43 containers** during peak load.
+
+## 4. Cold Start Strategy
+To minimize the delay when new containers are provisioned (cold start latency):
+1. **Lightweight Base Images:** Use Alpine or distroless images (e.g., `python:3.11-alpine`) so they pull faster over the network.
+2. **Pre-warming (Buffer Pool):** Maintain a buffer of idle containers (e.g., 10% of required capacity). For 43 containers, run ~47. The extra 4 handle sudden spikes instantly.
+3. **Lazy Loading:** Defer non-critical initialization until after the container has started accepting requests.
+
+## 5. Ghaymah Block Storage for Stateful Workloads
+While the API is mostly stateless, Ghaymah Block Storage is used for:
+- **Local Caching / ML Models:** Persistent storage for large datasets downloaded at startup.
+- **Session Data / Logs:** Persisting complex audit logs before they are shipped to centralized logging.
+- **Self-Managed Databases:** Ensuring data survives container restarts by mounting a volume like `/mnt/data`.