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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.

HardTechnical
0 practiced
Given monthly billing breakdown: S3 storage 40%, compute 35%, third-party services 15%, and network 10%, propose a 12-month prioritized program of initiatives for each category with estimated ROI and KPIs. Show sample calculations for the top three initiatives including implementation cost, monthly savings, payback period, and cumulative savings.
MediumTechnical
0 practiced
Explain how columnar file formats (Parquet, ORC) and compression codecs (snappy, zstd, gzip) affect storage size, bytes scanned during queries, CPU usage for encoding/decoding, and the overall cost/performance trade-offs. When would you choose a high-compression codec versus a fast codec for interactive versus nightly batch queries?
EasyTechnical
0 practiced
List common cloud object storage classes (for example AWS S3 Standard, S3 Intelligent-Tiering, S3 Infrequent Access, Glacier, Glacier Deep Archive) and explain their cost and access-latency trade-offs. For each access pattern (hot, warm, cold, archival) recommend an appropriate storage class and brief justification.
MediumSystem Design
0 practiced
Design a multi-tier storage strategy for a petabyte-scale data lake with Hot, Warm, Cold, and Archive tiers. Define criteria to move datasets between tiers (access frequency, business-critical tags, size), the expected cost savings per tier, and how you'd enforce and monitor tier transitions programmatically.
MediumTechnical
0 practiced
You ingest 10M small messages/sec into Kafka and storage and broker costs are rising. Propose batching/aggregation at producers, compression strategies, partitioning and retention/tiering changes to reduce cost while meeting consumer SLAs. Describe expected trade-offs for latency and CPU.

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