Infrastructure Scaling and Capacity Planning Questions

Operational and infrastructure level planning to ensure systems meet current demand and projected growth. Topics include forecasting demand headroom planning and three to five year capacity roadmaps; autoscaling policies and metrics driven scaling using central processing unit memory and custom application metrics; load testing benchmarking and performance validation methodologies; cost modeling and right sizing in cloud environments and trade offs between managed services and self hosted solutions; designing non disruptive upgrade and migration strategies; multi region and availability zone deployment strategies and implications for data placement and latency; instrumentation and observability for capacity metrics; and mapping business growth projections into infrastructure acquisition and scaling decisions. Candidates should demonstrate how to translate requirements into capacity plans and how to validate assumptions with experiments and measurements.

HardTechnical

57 practiced

You want to compute a custom application-level metric (e.g., tail latency per user action) from distributed tracing data and use it for autoscaling decisions. Describe a reliable, low-latency pipeline to compute, aggregate, and ship this metric to your autoscaler: choices for collection, aggregation windows, handling dropped traces, smoothing, and how to prevent the metric pipeline from causing noisy scaling.

MediumTechnical

75 practiced

Design a response plan for sudden DoS-like traffic spikes that saturate your public endpoints. Include immediate mitigation steps (CDN rate-limiting, WAF rules, edge filtering), capacity responses (queueing and graceful degradation), detection to differentiate legitimate viral growth from attack, and post-incident analysis actions.

EasyTechnical

68 practiced

Define capacity planning and headroom in the context of cloud infrastructure. Explain the difference between short-term autoscaling and long-term capacity planning, why both matter for production systems, and give concrete examples of metrics you would use for each (compute, storage, network and application-level).

HardSystem Design

74 practiced

Design an end-to-end experiment to validate a Kubernetes cluster autoscaler's behavior at scale (e.g., supporting 10,000 pods). Include how you would generate load, inject realistic pod start times and custom metrics, simulate node provisioning delays and failures, collect relevant telemetry, and define pass/fail criteria for scaling latency and stability.

MediumTechnical

58 practiced

You forecast a 3x increase in traffic after a feature launch. Before provisioning full capacity, describe experiments and measurements to validate assumptions: how to use shadowing, canary releases, incremental load tests, DB contention tests, and specific metrics to collect to decide whether to provision additional capacity.

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