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.

HardTechnical

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You must migrate a legacy low-latency system to the cloud. Compare lift-and-shift vs re-architecting for cloud-native performance and cost. For each option, list expected migration effort, performance risks, potential cost savings, and rollback strategies. Recommend a phased migration approach that minimizes user impact.

HardTechnical

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A client requires end-to-end encryption and high throughput for data processing. Discuss tradeoffs between terminating TLS at the edge, performing TLS at service-to-service level, using hardware crypto offload (e.g., AWS Nitro, HSM), and application-level encryption. Include latency, throughput, key management, and compliance implications.

EasyTechnical

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Describe how optimization decisions commonly impact cloud costs. Give two examples where improving performance increases cost (e.g., more compute) and two where optimization reduces cost (e.g., algorithmic improvements), and explain how you'd present these trade-offs to a cost-conscious customer.

MediumTechnical

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Which profilers, APM tools, and metrics would you use to detect and confirm a memory leak in containers running Python microservices? Explain differences between sampling and deterministic profilers, and tradeoffs for running them in production versus staging.

MediumTechnical

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Compare token bucket and leaky bucket algorithms for API rate limiting. Discuss centralized vs distributed enforcement, accuracy vs latency tradeoffs, memory and storage requirements, and how you'd enforce per-user limits at a scale of 100k RPS across multiple regions.

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