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:I would use phased rollout: shadow traffic, internal testing, limited tenant rollout, feature flags, compatibility checks, migration scripts, dashboards, and rollback plans. I would keep old and new paths running until correctness and performance are proven.

Model Answer:I would model the core entities, access patterns, and consistency requirements first. Then I would choose indexes and partition keys around the highest-volume reads and writes. I would avoid over-normalizing if low-latency reads are more important than relational flexibility.

Model Answer:The system should degrade gracefully: queue work if possible, return cached or partial results where safe, trip circuit breakers, stop retry storms, and expose clear status to operators and users. Critical paths should have fallback behavior.

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:I would pick a small input that includes the important edge behavior, then trace variables step by step. For example, I would show how pointers, maps, or counters change after each element and verify that the final answer matches the expected result.

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.

Model Answer:A stronger answer would include assumptions, alternatives considered, tradeoffs, complexity or impact, and a clear reason for the chosen approach. For senior roles, it should also include ownership, rollout, and operational concerns.