Large Scale Infrastructure Challenges Questions

Awareness of engineering and operational challenges at massive scale including global network optimization, multi region failover and redundancy, integration of cloud and on premise systems, security and compliance at scale, performance and latency for a global user base, cost optimization across large fleets, and maintaining reliability without exponential operational complexity. Candidates should demonstrate thinking about architecture patterns, trade offs, monitoring and incident response at scale, and strategies for evolving platform capabilities as load and feature sets grow.

EasyTechnical

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List five key metrics you would monitor for a globally distributed Redis/managed-cache deployment (memory usage, evictions, hit ratio, replication lag, commands_per_sec are examples). For each metric explain why it matters at scale and propose sensible alert thresholds or aggregation rules.

EasyTechnical

0 practiced

Explain the difference between an SLI, an SLO, and an SLA. For a globally distributed web API, give two concrete SLIs (one latency, one availability), propose reasonable SLO targets for each, and describe what operational actions you would take when the error budget is exhausted across the organization.

HardTechnical

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During a global incident you have only partial observability because the logging pipeline has delays. Explain how you would triage the incident, prioritize remediation actions, coordinate multi-region rollouts or rollbacks, and communicate status to stakeholders under uncertainty.

EasyTechnical

0 practiced

Define eventual consistency and strong (linearizable) consistency. Provide two production examples where eventual consistency is acceptable (for a global operator) and two examples where strong consistency is required. Discuss operational implications for testing and incident response.

HardTechnical

0 practiced

In Go or Python, design a highly concurrent reconciler that ensures desired vs actual state for 100k Kubernetes-like objects across 50 clusters. Provide architecture and pseudocode for worker pools, rate limiting to avoid API throttling, efficient batching, exponential backoff, and how to ensure eventual convergence without overloading control planes.

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