Capacity Planning and Forecasting Questions

Covers forecasting demand and planning infrastructure and platform capacity to meet expected business needs reliably and cost effectively. Candidates should be able to analyze historical usage and growth trends, build and validate capacity models, define capacity metrics and thresholds, estimate headroom and safety margins, and translate business growth scenarios into procurement or cloud provisioning plans and timelines. Includes storage and compute lifecycle planning such as archiving and retention strategies, upgrade and rollout planning to avoid disruption, and trade offs between overprovisioning and right sizing. Also addresses design for scale and redundancy, autoscaling and elasticity patterns, load balancing and failover planning, capacity testing and stress testing, monitoring and alerting for capacity signals, and techniques to measure and improve forecast accuracy. Finally it covers operational governance and decision making including cross team resource allocation, capacity reviews, cost optimization and budgeting, runbooks and change control, and alignment of capacity plans with service level objectives and business projections.

HardTechnical

81 practiced

Describe a capacity governance framework for a large organization. Include allocation policies, quota enforcement, chargeback/showback mechanisms, exception handling, capacity review boards, and automated enforcement via IaC and policy-as-code (e.g., OPA).

HardTechnical

77 practiced

Implement an efficient Python function that computes projected peak concurrent resource usage given a large stream of events represented as (arrival_time, processing_duration_seconds, resource_units_required). The algorithm must be better than O(n^2) for millions of events. Explain your approach, complexity, and memory trade-offs.

HardTechnical

88 practiced

You experienced an unexpected 3x traffic spike causing consumer lag and partial data loss in a streaming pipeline. Walk through a post-mortem: immediate mitigations, steps for root-cause analysis, capacity changes to prevent recurrence, monitoring and alert improvements, and stakeholder communication plan.

HardSystem Design

105 practiced

Architect a multi-tenant Kubernetes-based data platform where teams run data processing jobs. Noisy neighbors cause latency spikes and missed SLAs. Propose isolation strategies (resource quotas, vertical/horizontal autoscaling, node pools, taints/tolerations), admission controls, runtime classes, and monitoring to enforce SLOs while keeping costs reasonable.

EasyBehavioral

151 practiced

Tell me about a time when you produced a capacity forecast (storage, compute, or pipeline throughput) that was inaccurate. Describe the situation, the assumptions you made, how you discovered the error, and what actions you took to remediate and improve future forecasts. Use the STAR structure.

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