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

86 practiced

Propose a framework to continuously improve capacity forecast accuracy using automated backtesting, model selection, feature engineering (calendar effects, promotions), and feedback loops from incidents and on-call observations. Explain how to operationalize retraining, deploy new models safely, and measure improvement over time.

EasyTechnical

79 practiced

You have hourly request counts for the last 12 months. Which simple forecasting techniques (moving average, exponential smoothing, seasonal decomposition) would you try first and why? Describe quick validation steps (backtesting/holdout) and checks to detect seasonality or trend changes.

HardTechnical

105 practiced

After three consecutive months where capacity forecasts underestimated peak by ~30%, causing incidents, draft the core elements of an incident postmortem and a remediation plan. Focus on forecasting process failures, tooling/data-quality issues, ownership, governance changes, measurable outcomes, and timelines to prevent recurrence.

HardTechnical

94 practiced

Provide an algorithm in pseudocode to allocate limited compute capacity among competing services during a constrained period. Each service has a priority and SLO target; allocations should attempt to preserve higher-priority SLOs and be fair. Explain the algorithm's time complexity, limitations, and how you'd handle sudden changes in demand.

EasyTechnical

100 practiced

Explain the differences and primary goals of load testing, stress testing, soak testing, and spike testing. For each test type describe what capacity question it answers, example traffic patterns, and key metrics you would collect for a backend service.

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