Technical Leadership and Architectural Influence Questions

Demonstrating leadership in technical decisions at the architecture or system level. Candidates should prepare concrete examples where they identified architectural problems, evaluated alternative solutions and trade offs, proposed a preferred design, gained buy in from engineers and stakeholders, and drove implementation. Discuss systems thinking and long term impact on team velocity, code quality, reliability, and product features. Include examples of championing new tools or frameworks, leading migrations or refactors, negotiating trade offs between time to market and technical debt, and occasions when you reversed a decision based on new data. Emphasize communication of complex technical ideas, consensus building with peers, and measurable outcomes.

MediumSystem Design

66 practiced

Design a model-serving architecture to handle 10,000 requests per second with a p95 latency target of 50ms and a 100ms cold-start limit. Describe how you would use GPU autoscaling, inference batching, per-model replicas, caching layers (for precomputed responses/embeddings), warm pools, and load balancing. Justify trade-offs and failure modes.

EasyTechnical

73 practiced

Explain the trade-offs between batching requests on GPUs (higher throughput) and per-request latency/fairness for an inference microservice. Propose one or two heuristics or algorithms (e.g., max-batch + max-wait, latency-first vs throughput-first queues) for dynamic batching in a low-latency setting and discuss how you would measure success.

EasyTechnical

77 practiced

Explain eventual consistency in plain terms and give two concrete AI-system examples where eventual consistency is acceptable (for example, model metadata propagation, cache invalidation for embeddings). For each example, describe the acceptable staleness window and when you would instead require strong consistency.

MediumTechnical

86 practiced

Design a distributed rate limiter (token-bucket) to enforce per-user request limits across many stateless inference instances using Redis as the coordination layer. Describe the algorithm or pseudo-code (including atomic Redis operations or Lua), how you guarantee atomic updates, and how you mitigate clock skew and Redis hot keys.

HardTechnical

78 practiced

Draft a governance policy for integrating a third-party LLM API as a component in your product. Cover latency expectations, cost controls and throttles, vendor lock-in risk mitigation, fallback mechanisms for outages, privacy/data handling requirements, and an escalation path for unacceptable model outputs.

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