Focuses on how architectural decisions and design patterns affect operating cost and total cost of ownership. Interviewees should be able to reason about trade offs such as managed services versus self managed components, always on virtual machines versus event driven or serverless approaches, reserved versus on demand capacity, use of spot or preemptible instances, and multi region or edge placement. Candidates should demonstrate techniques for reducing cost through storage class selection and lifecycle policies, caching and batching, query and workload optimization, data transfer minimization, and workload isolation. The topic also covers modeling and communicating cost trade offs, estimating ongoing operating expense for alternative designs, and choosing architecture that balances budget constraints with reliability, performance, and engineering effort.
HardSystem Design
0 practiced
Design a cost-aware hybrid-cloud architecture for a regulated enterprise that must keep PII on-prem but burst to cloud for peak loads. Requirements: support 10k concurrent users, 99.99% availability at peak, minimize annual OPEX, and limit data transfer costs. Provide data flow, replication strategy, burst model (reserved vs on-demand), and how you'd compute TCO including staff and networking.
EasyTechnical
0 practiced
A customer stores 100 TB of objects with access pattern: 10% hot, 30% warm, 60% cold. Propose a storage class and lifecycle policy strategy (examples: standard, infrequent-access, archive tiers) to lower monthly costs while meeting retrieval SLAs. Explain assumptions, minimum retention penalties, transition/read costs, and how you would model monthly cost and risks.
EasyTechnical
0 practiced
Explain Total Cost of Ownership (TCO) in the context of cloud architectures. Define the component parts (one-time capital vs ongoing operating expenses), list common cloud line-items (compute, storage, egress, licensing, support, personnel, backups, monitoring, downtime), and describe why TCO matters when comparing managed services versus self-managed deployments. Give at least two examples of costs that are commonly overlooked.
MediumTechnical
0 practiced
A data pipeline ingests 5 TB/day and needs hourly aggregations. Compare always-on streaming (near real-time) vs micro-batch (hourly) processing in terms of cost, latency, and operational complexity. Recommend optimizations (file format, partitioning, compaction, compression) that reduce compute and query costs while keeping acceptable freshness.
EasyTechnical
0 practiced
Explain the difference between instance reservations and compute savings plans (or equivalent) across cloud providers. Discuss flexibility differences (region/instance family constraints), common billing terms, and recommendations for which workload types to commit (steady baseline vs variable peak).
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