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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.

HardSystem Design
0 practiced
You manage petabytes of data where 5% must be hot (low-latency queries), 25% warm (ad-hoc queries), and 70% cold/archival. Design a multi-tier datastore solution that optimizes for cost and access SLA. Compare options: object storage plus query-on-object (e.g., Athena), warm data in columnar stores (Parquet on optimized storage or OLAP), and hot data in low-latency NoSQL. Address retrieval costs, egress, indexing needs, and automated lifecycle migration.
MediumTechnical
0 practiced
Explain how to design Terraform modules and CI checks to enforce resource tagging (team, product, environment, cost-center) and prevent untagged or overprovisioned resources from being applied. Include examples of policy-as-code (OPA/Gatekeeper or Sentinel), pre-apply CI checks, and a remediation strategy for non-compliant existing resources.
EasyBehavioral
0 practiced
Tell me about a time when you proposed or implemented a cost-saving change in production. Use the STAR structure: describe the situation, the task you were given, the actions you took, the measurable results (dollars saved or percent reduction), and what you automated to sustain the savings.
EasyTechnical
0 practiced
Explain spot (preemptible) instances, their cost benefits, typical interruption modes, and examples of workloads that are a good fit versus bad fits. Describe three simple engineering strategies to mitigate interruptions when using spot instances.
MediumSystem Design
0 practiced
Design a caching strategy for a read-heavy product catalog API with 50M requests/day where only 2% of items change daily. Constraints: 99.95% availability, average origin latency 200ms, limited budget for cache nodes. Describe cache layers (CDN/edge, regional caches, in-memory application caches), invalidation and TTL strategies, cache key design, and estimate expected reduction in origin load.

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