Performance Monitoring & Observability Questions
Instrumenting systems so performance is continuously measured and regressions are visible. Covers performance metrics and SLIs, dashboards and time-series signals, tracing, alerting on latency and saturation, and using telemetry to guide tuning. Focuses on the ongoing measurement loop rather than one-off profiling.
EasyTechnical
49 practiced
Basic capacity-planning exercise: your API handles peak 500 requests per second. Each request consumes 30 milliseconds of CPU on one core on average. You require 50% headroom for bursts and 1.5x redundancy for fault tolerance. All servers are 4-core machines. Estimate the minimum number of servers required and show your calculations.
HardTechnical
74 practiced
Network troubleshooting: you see 1% packet loss and excessive TCP retransmits between datacenters. Describe a set of tests and captures you would perform (including tcpdump, iperf, mtr, and host-side counters), exactly what signatures you would look for in packet captures, and how you would determine if the problem is congestion, faulty hardware, or a misconfigured load balancer.
HardTechnical
53 practiced
A noisy neighbor VM in a shared hypervisor is causing CPU and storage starvation for others. Propose a set of isolation and mitigation techniques spanning cgroups, CPU pinning, storage QoS, hypervisor scheduler tuning, and capacity policies. For each technique describe expected effectiveness and operational trade-offs.
HardTechnical
56 practiced
Technical-coding/runbook: Provide a safe, idempotent bash script template (pseudocode acceptable) that an on-call engineer can run to triage a high disk I/O incident. The script should perform read-only checks: gather iostat/xstat, identify top io-consuming processes, capture recent dmesg lines, and output a prioritized short report of findings. The script must avoid writing to disks and must include comments describing each step.
EasyTechnical
50 practiced
Describe how you would establish performance baselines and normal ranges for a fleet of 200 servers running mixed workloads. Explain what data to collect, appropriate time windows, how to account for weekly and seasonal patterns, and how long you would retain metric history to be useful for capacity planning.
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