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.

EasyTechnical

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Explain capacity planning in the context of data engineering. Cover objectives, typical outputs (forecasts, headroom, procurement timelines), common metrics (throughput, storage growth, latency, concurrency) and time horizons (short/medium/long). Give examples of when you'd choose short-term autoscaling vs long-term procurement.

MediumTechnical

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You have the following baseline: 100,000 daily active users (DAU), each generates 200 events/day on average, average event size is 1.2 KB. DAU growth is expected at 5% month-over-month. Build a 12-month capacity model for daily ingestion (GB/day) and total storage assuming full retention for 12 months. Show calculations and propose a 20% safety margin. State all assumptions.

MediumSystem Design

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Design a multi-region failover plan for a data platform with RPO = 1 hour and RTO = 30 minutes. Consider replication options (sync vs async), storage replication costs, cross-region bandwidth, bootstrap times, and trade-offs to minimize cost while meeting RPO/RTO.

MediumTechnical

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How would you align the technical capacity plan with product roadmaps and business forecasts? Describe the data inputs you need (feature releases, marketing campaigns), cadence for updates, risk buffers, and how you would manage conflicting priorities across product teams.

HardTechnical

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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.

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