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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
54 practiced
Implement a concurrency-safe batcher in Python that collects items submitted by multiple coroutines within a 100ms window and calls a provided async function process_batch(items). Interface: class Batcher with async def add(item) -> result. Requirements: preserve ordering within a batch, map individual add callers to their result, and retry the entire batch up to 3 times on process_batch failure. Provide code or clear pseudocode.
MediumTechnical
48 practiced
Describe a method to estimate per-request CPU and memory cost for a high-volume service using sampling and statistical inference. Explain how you would design the sampling strategy (random vs stratified), control instrumentation overhead, extrapolate to total traffic, and present confidence intervals to stakeholders.
HardTechnical
52 practiced
A leader asks you to cut a service's cost by 50%. Provide a structured checklist of low-risk, medium-risk, and high-risk tactics (for example rightsizing, buying commitments, caching, moving compute, re-architecting for micro-batching), and for each tactic estimate typical time-to-value, typical percentage savings range, and main operational risks. Explain how you would prioritize and stage these actions.
EasyTechnical
39 practiced
Describe cache eviction policies and invalidation strategies used in distributed caches (LRU, LFU, TTL, cache-aside, write-through, write-back). For a read-heavy product catalog API, explain which strategy you would choose and why, including how that choice impacts cost and origin load.
MediumSystem Design
82 practiced
Design a multi-tier storage and lifecycle policy for a service that stores 5 PB of objects with the following access profile: 70% accessed within 7 days, 20% accessed between 7-90 days, 10% rarely accessed (>90 days). Requirements: minimize monthly cost subject to retrieval latency SLAs (hot <50ms, nearline <2s, archive <12 hours). Describe which storage classes to use, lifecycle transitions, estimated cost trade-offs, and key validation metrics.

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