docs: append missing answers to fulfill all exam criteria

q1-deploy-monitor/health-check.sh

@@ -1,6 +1,6 @@
 #!/usr/bin/env bash
 
-LOG_FILE="/Users/mac/a1/monitor/monitor.log"
+LOG_FILE="/Users/mac/a1/q1-deploy-monitor/monitor.log"
 
 # Ensure log file exists
 mkdir -p "$(dirname "$LOG_FILE")"

q2-postmortem/postmortem-report.md

@@ -26,3 +26,15 @@
 - **Short-term:** Implement a memory-based auto-scaling rule (Scale out when Memory > 70%).
 - **Medium-term:** Setup early-detection alerts for memory utilization reaching 80% to warn the team *before* an OOMKilled event occurs.
 - **Long-term:** Profile the application to identify memory bottlenecks or leaks.
+
+## 5. Auto-Scaling Policy
+To prevent repeating this incident, the platform's auto-scaling group must be configured as follows:
+- **Scale-Out Policy:** Add 1 container instance when Average Container Memory > 70% for 2 minutes.
+- **Scale-In Policy:** Remove 1 container instance when Average Container Memory < 40% for 5 minutes.
+- **CPU Backup Policy:** Scale out if Average CPU > 75% for 2 minutes.
+
+## 6. Early Detection
+To detect this issue before the container crashes:
+- Configure alerts on the metric `container_memory_usage_bytes` (or equivalent).
+- **Warning Alert:** Container Memory > 80% (Sustained for > 3 minutes) - triggers Slack/Teams notification.
+- **Critical Alert:** Container Memory > 90% (Sustained for > 2 minutes) - triggers PagerDuty to wake up on-call engineer for immediate manual mitigation.

q3-cicd/workflow.yml

@@ -58,3 +58,20 @@ jobs:
         run: |
           echo "Deploying production-${{ github.sha }} to ghaymah systems..."
           # Example CLI command: ghaymah deploy --image ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:production-${{ github.sha }} --env production
+
+# =====================================================================
+# Q3 Additional Requirements Documentation
+# =====================================================================
+#
+# 3. Difference between Staging and Production:
+# - Staging (`staging`): A pre-production environment used for QA and integration testing. 
+#   It uses sanitized/dummy data and is scaled down. Deploys happen automatically upon merging to main.
+# - Production (`production`): The live environment for real users. Contains sensitive, live data. 
+#   Requires strict access controls and a **Manual Approval** step in GitHub Actions before deployment.
+#
+# 4. Integration with Ghaymah CLI:
+# To deploy manually or from a runner using the CLI:
+# 1. Install: `curl -sL https://cli.ghaymah.systems/install.sh | bash`
+# 2. Login: `ghaymah login --token $GHAYMAH_TOKEN`
+# 3. Push: `docker push registry.ghaymah.systems/my-org/myapp-api:latest`
+# 4. Deploy: `ghaymah deploy --name myapp-api --image registry.ghaymah.systems/my-org/myapp-api:latest --port 8080 --env production`

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`.

q5-mithal-monitor/dashboard.html

@@ -159,5 +159,22 @@
         }
         tbody.innerHTML = rows;
     </script>
+
+    <!--
+    =====================================================================
+    Q5: Deployment Instructions (انشر الـ dashboard على ghaymah.systems)
+    =====================================================================
+    To deploy this dashboard on Ghaymah.systems:
+    1. Create a simple Nginx Dockerfile in this directory:
+       FROM nginx:alpine
+       COPY dashboard.html /usr/share/nginx/html/index.html
+       EXPOSE 80
+    2. Build and push the image:
+       docker build -t registry.ghaymah.systems/my-org/mithal-dashboard .
+       docker push registry.ghaymah.systems/my-org/mithal-dashboard
+    3. Deploy using Ghaymah CLI:
+       ghaymah deploy --name mithal-dashboard --image registry.ghaymah.systems/my-org/mithal-dashboard --port 80 --env production
+    =====================================================================
+    -->
 </body>
 </html>

q5-mithal-monitor/data/metrics.json

@@ -0,0 +1,42 @@
+[
+  {
+    "timestamp": "2026-07-27T11:27:47.907297Z",
+    "latency_ms": 601.08,
+    "status_code": 200,
+    "uptime": true,
+    "ssl_status": "Error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1081)",
+    "ssl_days": 0,
+    "dns_time_ms": 354.55,
+    "search_time_ms": 637.0
+  },
+  {
+    "timestamp": "2026-07-27T11:28:50.010086Z",
+    "latency_ms": 627.2,
+    "status_code": 200,
+    "uptime": true,
+    "ssl_status": "Error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1081)",
+    "ssl_days": 0,
+    "dns_time_ms": 64.76,
+    "search_time_ms": 599.89
+  },
+  {
+    "timestamp": "2026-07-27T11:29:51.597790Z",
+    "latency_ms": 635.14,
+    "status_code": 200,
+    "uptime": true,
+    "ssl_status": "Error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1081)",
+    "ssl_days": 0,
+    "dns_time_ms": 86.34,
+    "search_time_ms": 721.88
+  },
+  {
+    "timestamp": "2026-07-27T11:30:53.354004Z",
+    "latency_ms": 596.43,
+    "status_code": 200,
+    "uptime": true,
+    "ssl_status": "Error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1081)",
+    "ssl_days": 0,
+    "dns_time_ms": 71.37,
+    "search_time_ms": 605.94
+  }
+]

q5-mithal-monitor/mithal.log

@@ -0,0 +1,2 @@
+/Users/mac/a1/q5-mithal-monitor/monitor.py:36: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).
+  "timestamp": datetime.utcnow().isoformat() + "Z",

q5-mithal-monitor/monitor.py

@@ -9,7 +9,7 @@ import dns.resolver
 
 TARGET_URL = "https://mithal.space"
 TARGET_DOMAIN = "mithal.space"
-DATA_FILE = "/Users/mac/a1/mithal_monitor/data/metrics.json"
+DATA_FILE = "/Users/mac/a1/q5-mithal-monitor/data/metrics.json"
 
 def check_ssl(domain):
     context = ssl.create_default_context()