Model Answer:Start with requirements: channels, scale, latency, retries, preferences, templates, and audit logs. Use API service for request ingestion, queue for async fan-out, workers per channel, template service, preference store, and delivery status store. Add idempotency keys, rate limits, retry with dead-letter queues, metrics, and provider failover. At lead/staff level, I would also define ownership boundaries, migration stages, operational runbooks, and review checkpoints with adjacent teams.

Model Answer:Likely bottlenecks include database hotspots, queue backlog, third-party dependency failures, cache stampedes, and uneven traffic across tenants. I would address them with partitioning, backpressure, retries with jitter, circuit breakers, rate limits, observability, and graceful degradation.

Model Answer:Use a sliding window because all costs are non-negative. Keep left, right, and current sum. Expand right, shrink left while sum exceeds B, and track the maximum length. This runs in O(n) time and O(1) extra space.

Model Answer:I would test empty or minimum-sized input, all values equal, no valid answer, maximum constraints, repeated values, boundary budget values, and cases where the best answer appears at the beginning or end. I would also include one stress-style test to confirm the algorithm stays within time limits.

Model Answer:I would first confirm the current complexity. If it is already O(n) or O(n log n), I would focus on memory usage, streaming input, avoiding unnecessary copies, and using iterative logic. If the solution is quadratic, I would look for hashing, prefix sums, sorting, two pointers, or a monotonic structure depending on the problem shape.

Model Answer:Use STAR. Situation: production issue affected users. Task: restore service and communicate clearly. Action: checked dashboards, narrowed the failing dependency, rolled back or disabled the risky feature, updated stakeholders, and created a follow-up RCA. Result: service recovered and the team added alerts/tests to prevent recurrence.

Model Answer:I would ask for scale, constraints, success metric, expected tradeoffs, and whether the interviewer wants a high-level design or implementation-level details. Clarifying early prevents solving the wrong problem.