Describe a distributed service you designed or operated and the key reliability tradeoffs you made.I would explain the service boundaries, workload characteristics, failure modes, and consistency requirements. I would also cover how I used mechanisms such as timeouts, retries, idempotency, backpressure, replication, and observability to meet defined SLOs.
How would you investigate a sustained increase in latency for a multi-tenant managed Postgres platform?I would first scope the impact by tenant, region, endpoint, and workload type using metrics, traces, and logs. I would then examine database saturation, connection pools, slow queries, storage and network behavior, queue depth, recent deployments, and noisy-neighbor signals before mitigating the immediate issue and documenting preventive actions.
How do you decide whether an asynchronous workflow should use a queue, event stream, or synchronous API call?I assess latency requirements, delivery guarantees, ordering, replay needs, coupling, throughput, and failure handling. Synchronous APIs fit immediate bounded work, while queues or streams are preferable for decoupled, durable, retryable, and scalable background processing.
What should a useful SLO and alerting strategy look like for a core platform API?I would define user-impacting indicators such as availability, latency, and correctness, set targets based on product expectations, and use error budgets to guide operational decisions. Alerts should be actionable, symptom-oriented, tied to meaningful burn rates, and supported by dashboards and runbooks.
How would you introduce infrastructure changes safely using Pulumi or Terraform?I would use version-controlled infrastructure definitions, isolated environments, peer review, automated plans, policy checks, and staged rollout procedures. For high-risk changes, I would validate rollback paths, monitor post-deployment signals, and avoid coupling irreversible data changes with broad infrastructure releases.
