Capacity Planning and Resource Optimization Questions

Covers forecasting, provisioning, and operating compute, memory, storage, and network resources efficiently to meet demand and service level objectives. Key skills include monitoring resource utilization metrics such as central processing unit usage, memory consumption, storage input and output and network throughput; analyzing historical trends and workload patterns to predict future demand; and planning capacity additions, safety margins, and buffer sizing. Candidates should understand vertical versus horizontal scaling, autoscaling policy design and cooldowns, right sizing instances or containers, workload placement and isolation, load balancing algorithms, and use of spot or preemptible capacity for interruptible workloads. Practical topics include storage planning and archival strategies, database memory tuning and buffer sizing, batching and off peak processing, model compression and inference optimization for machine learning workloads, alerts and dashboards, stress and validation testing of planned changes, and methods to measure that capacity decisions meet both performance and cost objectives.

HardBehavioral

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Behavioral: Tell me about a time when you had to make a capacity-related trade-off (performance vs cost) on an AI system. What was the situation, what options did you consider, what decision did you make, and what were the outcomes? Use the STAR format.

MediumTechnical

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Scenario: You need to place multiple versions of a large model (e.g., for A/B testing) while minimizing extra memory cost on GPU nodes. Propose a placement and memory-sharing strategy and explain how to avoid interference and ensure fair comparison metrics between variants.

MediumTechnical

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Design a stress and validation testing plan you would run before rolling out a cluster size or instance type change for an inference fleet. Include load profiles, test data, synthetic traffic vs production replay, success criteria, and rollback criteria.

HardTechnical

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Create a two-year capacity roadmap to support a target of 10× model size growth and doubling inference QPS each year. Cover compute, storage, network, budget forecasts, procurement lead times, cloud vs on-prem trade-offs, and organizational implications (hiring, SRE effort).

MediumTechnical

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Scenario: An inference fleet begins showing rising p99 latency during peak hours after a model update. Outline an incident response and capacity investigation plan: immediate mitigation steps, telemetry to examine, capacity adjustments to try, and post-incident capacity changes you might propose.

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