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Cloud Cost Optimization and Financial Operations Questions

Covers strategies and organizational practices for minimizing and managing cloud and infrastructure spend while balancing performance, reliability, and business priorities. Candidates should understand cloud cost drivers such as compute, storage, data transfer, and managed services; pricing models including on demand pricing, reserved capacity commitments, savings plans, and interruptible or spot offerings; and engineering techniques that reduce spend such as rightsizing, autoscaling, storage tiering, caching, and workload placement. This topic also includes financial operations practices for continuous cost management and governance: resource tagging and cost allocation, budgeting and forecasting, chargeback and showback models, anomaly detection and alerting, cost reporting and dashboards, and processes to gate changes that affect spend. Interviewees should be able to estimate recurring costs and total cost of ownership, identify and quantify optimization opportunities, weigh trade offs between cost and business objectives, and describe tools and metrics used to monitor and communicate cost to stakeholders.

EasyTechnical
0 practiced
How would you estimate the cost per 1,000 inferences for a deployed model? List the inputs you need (e.g., instance hourly price, average latency, concurrency) and provide a worked numeric example for a CPU-based service that takes 50ms per inference and runs on a 4-vCPU instance costing $0.20/hour.
HardSystem Design
0 practiced
Design a training platform that leverages spot instances across multiple availability zones and instance pools for resilience. Provide details on checkpointing cadence, how to schedule retries, policies to select instance pools, and a cost-vs-completion-time model that guides when to fall back to on-demand instances.
HardTechnical
0 practiced
You have three methods to reduce production inference cost: pruning, distillation, and quantization. Propose an experiment design to quantify cost savings and accuracy loss across these methods for a vision model served at 100M inferences/day. Include measurement plan, sample sizes, and decision criteria for production rollout.
MediumTechnical
0 practiced
Describe a robust approach to use spot instances for distributed training: include checkpointing frequency, job fragmentation (smaller tasks vs single large job), multi-pool bidding, and how to model expected completion time given historical preemption rates.
MediumSystem Design
0 practiced
Design Kubernetes node pool layouts for ML workloads: GPU node pool(s), CPU spot pool, and general-purpose pool. Explain how taints/tolerations, node affinity, and autoscaling settings should be configured to minimize cost while ensuring critical jobs get stable capacity.

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