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Optimization and Technical Trade Offs Questions

Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.

EasyTechnical
55 practiced
Implement a simple token-bucket rate limiter in Python for a single process that supports allow_request(key, timestamp) -> bool. Parameters: capacity and refill_rate (tokens per second). The implementation only needs to be correct single-threaded; explain time and space complexity and trade-offs of your approach.
MediumTechnical
88 practiced
Explain the trade-offs between synchronous and asynchronous replication for a database used by an e-commerce platform. Discuss write latency, durability guarantees, failover complexity, consistency, throughput, and cost. From an SRE perspective, which factors weigh most for cart checkout transactions versus analytics workloads?
MediumTechnical
49 practiced
Implement a concurrent LRU cache in Go supporting Get(key) and Put(key, value) with approximate LRU eviction for higher throughput under heavy concurrency. Use sharding (for example, 16 shards) to reduce lock contention. Provide code or structured pseudocode and discuss the trade-offs between strict LRU and approximate approaches for SRE workloads.
HardTechnical
48 practiced
Cross-region tail latency is hurting a geo-replicated service. Propose strategies such as read-local, read-repair, CRDTs, leader-follower optimizations, regional caching, and edge routing to reduce p99. For each approach, discuss consistency implications, added cost, operational complexity, and when it is appropriate.
MediumTechnical
62 practiced
A memory-hungry monolith drives up costs. Compare strategies: (1) refactor to reduce memory usage, (2) split into microservices, (3) move workloads to batch processing. For each option, discuss performance characteristics, operational complexity, rollout risk, and long-term cost implications.

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