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Cost Optimization at Scale Questions

Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.

MediumTechnical
39 practiced
Your organization must cut data infrastructure spend by 20% in 6 months. Sketch a prioritized program focused on initiatives the data engineering team can own: quick wins (low effort, high impact), tooling/instrumentation investments, and behavioral or governance changes. Include how you'd measure and report progress.
MediumTechnical
51 practiced
Compare serverless ETL offerings (e.g., BigQuery serverless, AWS Glue serverless) vs provisioned clusters for large-scale data processing. Discuss cost trade-offs for bursty versus steady workloads, cold-start implications, per-query pricing models, and operational overhead considerations.
EasyTechnical
48 practiced
List common sources of network egress charges in cloud data platforms (for example cross-region replication, third-party API calls, sharing datasets with customers, BI dashboard exports) and give five practical mitigations to reduce egress costs. Explain how you would estimate the dollar impact of each mitigation.
MediumTechnical
42 practiced
Your ETL cluster shows wide runtime variability and frequent overprovisioning leading to elevated costs. Propose an autoscaling and scheduling strategy to reduce cost while meeting job SLOs. Include scaling signals, cooldowns, priority/preemption rules, and metrics you'd monitor to prove improvement.
MediumSystem Design
44 practiced
Design a cost-optimized ingestion pipeline to handle 100 TB/day of event data into object storage and downstream analytics store. Include choices for batching versus streaming, compression and codec selection, partitioning, compute sizing, retry/backpressure, cross-region replication, and the primary cost levers you'd monitor.

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