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Meta·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

Meta system design round for a software engineering role. One big open-ended question about a downed server that kept expanding into architecture territory. Felt like it went okay but the scope was wide enough that I kept second-guessing whether I was going deep enough or just listing things.

Questions Asked (1)

Q1

A standalone web server is suddenly serving a down page. Walk through how you'd diagnose the root cause across different failure scenarios, network, DNS, disk, memory, CPU, application errors, dependency failures, a bad deploy, etc. Then talk through what architectural and operational changes you'd make to improve resilience going forward.

System DesignRoot Cause AnalysisTechnical Trade-offs
Author's notes

This one is deceptively wide.

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AI HintsAI Generated

Suggested Approach

Start by outlining a systematic diagnostic process that moves from external to internal factors, covering network, DNS, disk, memory, CPU, application, dependencies, and deploy issues. Then discuss both immediate remediation and long-term architectural and operational improvements to prevent recurrence and improve resilience.

Pro tip: Emphasize the importance of observability and automation in both diagnosis and prevention; mention specific tools and metrics you'd monitor, and how you'd use them to quickly identify the root cause.

1. Verify the Symptom and Scope

Confirm the down page is real and determine if it's isolated to one server or widespread. Check monitoring dashboards, logs, and try accessing from different networks.

2. Check External Factors

Rule out network issues (e.g., load balancer, firewall, routing), DNS resolution, and upstream dependencies (e.g., database, third-party APIs). Use tools like dig, traceroute, curl, and check dependency health.

3. Inspect Server Resources

Check CPU, memory, disk I/O, and disk space using top, free, df, iostat, etc. Look for resource exhaustion or bottlenecks that could cause the application to fail.

4. Analyze Application and Logs

Review application logs, error messages, and stack traces. Check for recent code changes or configuration updates. Verify if the application process is running and listening on the correct port.

5. Propose Resilience Improvements

Suggest architectural changes like redundancy, auto-scaling, circuit breakers, and operational changes like better monitoring, alerting, canary deployments, and chaos engineering.

Key Points to Mention

  • Systematic troubleshooting methodology (e.g., OSI model, divide and conquer)
  • Use of monitoring and observability tools (e.g., Prometheus, Grafana, ELK, Datadog)
  • Common failure scenarios and their signatures (e.g., DNS resolution failure, disk full, memory leak, CPU spike, dependency timeout, bad deploy)
  • Immediate mitigation steps (e.g., rollback, restart, failover)
  • Long-term resilience strategies (e.g., load balancing, auto-scaling, multi-AZ deployment, circuit breakers, graceful degradation)
  • Operational best practices (e.g., canary releases, blue-green deployments, chaos engineering, post-mortems)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.