I started talking about dashboards and alerting thresholds and then realized I was basically describing reactive monitoring, not proactive ownership.
Structure your answer around a proactive monitoring and incident response loop: first, ensure you have visibility into the system's health; second, define clear escalation and mitigation procedures; third, continuously improve based on learnings. Emphasize automation, documentation, and communication to show you can handle on-call responsibilities reliably at scale.
Pro tip: Mention that you treat on-call as a engineering problem: if you get paged for the same issue twice, you automate the fix or improve the alert to prevent future pages. This shows you think beyond firefighting and focus on long-term reliability.
Ensure comprehensive monitoring, logging, and alerting are in place for the server and its dependencies. Know what 'normal' looks like by defining and tracking key SLOs/SLIs.
Have clear, step-by-step runbooks for common failure scenarios and know who to escalate to if an issue is beyond your expertise or access. Test these procedures regularly.
When an alert fires, follow a structured approach: acknowledge, assess impact, mitigate (e.g., rollback, failover), and communicate status to stakeholders. Prioritize restoring service over finding root cause.
After an incident, lead a blameless postmortem to identify root cause and action items. Implement fixes, add tests, and improve monitoring to prevent recurrence.
Identify repetitive on-call tasks and automate them (e.g., auto-remediation, self-healing). This reduces human error and allows you to focus on strategic work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a chronological narrative that covers detection, triage, mitigation, root cause analysis, and prevention. Emphasize how you prioritize user impact, communicate clearly, and balance speed with thoroughness. Use a specific example to make your answer concrete and show ownership.
Pro tip: Meta values a 'move fast' mentality, but in incidents, they also value blameless post-mortems and systemic fixes. Highlight how you automate detection and prevention to reduce future incidents.
Explain how you become aware of the incident (e.g., monitoring, alerts, user reports). Mention tools like Datadog, Prometheus, or internal systems, and how you validate the alert.
Describe how you quickly assess severity, impact, and scope. Include steps like checking dashboards, logs, and recent changes to determine the blast radius.
Outline immediate actions to stop the bleeding (e.g., rollback, feature flag, scaling). Stress the importance of clear communication with stakeholders and incident channels.
Explain how you investigate the underlying cause using techniques like the 5 Whys, timeline reconstruction, and code review. Mention collaboration with other teams.
Describe the permanent fix, validation, and post-mortem. Include action items to prevent recurrence, such as adding tests, monitoring, or process improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by systematically categorizing potential causes across the request path—client, network, server, and dependencies—then describe how you would isolate each layer using metrics, logs, and tracing. Emphasize a data-driven approach: measure first, then narrow down using tools like distributed tracing and profiling.
Pro tip: Mention that you'd check for recent changes (deploys, config updates) early, as most latency regressions are caused by recent modifications. Also, highlight the importance of establishing a baseline to distinguish normal from abnormal behavior.
Clarify what 'hanging' or 'slow' means: is it all requests or specific endpoints? When did it start? Gather concrete data like latency percentiles and error rates.
Review recent deployments, configuration changes, or infrastructure updates that could correlate with the issue. Roll back if a clear culprit is found.
Use monitoring and tracing to determine if the delay is in the client, network, load balancer, application server, or downstream dependencies. Look at metrics like CPU, memory, I/O, and network latency.
If the issue is in the application, use profiling, thread dumps, and logs to identify bottlenecks such as slow database queries, lock contention, or inefficient code.
Once the root cause is identified, implement a fix, test it in a staging environment, and monitor to ensure the issue is resolved without introducing new problems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
ps aux, systemctl list-units, netstat or ss for open ports.
Start by clarifying the scope (Linux/Unix vs Windows, local vs remote) and then walk through a systematic, layered approach: first get a high-level view of processes and listening ports, then drill into resource usage and service management. Emphasize using standard tools like ps, top, netstat/ss, systemctl, and lsof, and mention how you'd automate or script the discovery for repeatability.
Pro tip: Demonstrate operational maturity by mentioning that you'd first check for configuration management or orchestration agents (e.g., Chef, Puppet, Kubernetes) to understand the intended state, and always consider security implications like not running untrusted commands or exposing sensitive data.
Ask about the OS (Linux, Windows), whether you have shell access, and if it's a container or VM. This determines the toolset and commands you'll use.
Use commands like `ps aux`, `top`, or `htop` to list running processes sorted by CPU/memory. On Windows, use Task Manager or `Get-Process` in PowerShell.
Run `netstat -tulpn` or `ss -tulpn` to see which ports are open and which processes own them. Use `lsof -i` for a more detailed view.
Check `systemctl list-units --type=service` (systemd) or `service --status-all` (SysV) to see managed services. Also look at cron jobs and init scripts.
Map processes to services, note resource usage, and consider using tools like `pstree` or `ps -ef --forest` to understand parent-child relationships. Summarize what's running and why.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CPU, memory, disk I/O, network bandwidth, file descriptors, and open connections.
Structure your answer by categorizing finite resources into compute, memory, storage, and network, then explain how each can become a bottleneck under load. Emphasize that bottlenecks often shift as you scale, and mention monitoring and profiling to identify them.
Pro tip: Tie each resource to a concrete example of a bottleneck and a mitigation strategy, showing you understand trade-offs in real systems. Mention that at Meta's scale, even small inefficiencies in these resources can cause cascading failures.
List the main finite resources: CPU, memory, disk I/O, network bandwidth, and file descriptors/connections. Briefly define each.
For each resource, describe how it can become saturated (e.g., CPU-bound tasks, memory leaks, disk thrashing, network congestion) and the symptoms (latency, errors, throughput drops).
Mention tools and metrics (e.g., top, vmstat, iostat, netstat, profiling) to identify which resource is the bottleneck.
Explain strategies to alleviate bottlenecks (caching, sharding, compression, connection pooling) and the trade-offs involved (e.g., memory vs. CPU, latency vs. throughput).
Conclude by emphasizing that identifying and balancing these resources is crucial in system design, especially for scalable services.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than it should have.
Start by framing the problem around the non-technical person's constraints: limited time, budget, and expertise. Then propose a layered set of best practices that prioritize simplicity, automation, and clear visibility, focusing on what gives the most operational leverage with minimal effort. Emphasize that the goal is to make the system self-healing and self-reporting as much as possible.
Pro tip: Recommend starting with the 'three pillars' of observability—logs, metrics, and alerts—but tailor them to the person's scale: e.g., use a hosted logging service with a free tier, set up uptime monitoring with SMS alerts, and automate daily backups to cloud storage. This shows you can right-size solutions to the user's context, a key trait at Meta where scalability and pragmatism go hand in hand.
Ask about the website/service architecture, traffic, budget, and the person's technical comfort. This ensures recommendations are actionable and not overwhelming.
Set up uptime monitoring, error tracking, and resource usage alerts using simple tools (e.g., UptimeRobot, Sentry, CloudWatch). Ensure alerts go to a channel the person checks regularly.
Schedule automatic backups, security updates, and log rotation. Use managed services or simple scripts to reduce manual toil.
Create a simple document with common issues and step-by-step fixes. Include contact info for when to escalate.
Set a monthly reminder to review alerts, costs, and performance. Adjust practices as the system grows or changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.