Question 1

Tell me about a time you designed a system that needed to scale significantly.

Accepted Answer

Give the scale numbers up front — requests per second before and after, what broke first — then the specific changes: caching layers, read replicas, queueing writes, or sharding. Interviewers want to hear that you measured the bottleneck before scaling it, and what you'd hit next at 10x.

Question 2

Describe a production incident you resolved. What was your debugging process?

Accepted Answer

Use the incident timeline structure: detection (alert or user report), triage (mitigate first, root-cause later), the narrowing process, and the fix. Close with the postmortem outcome — what monitoring, runbook, or safeguard exists now because of that incident. Blameless framing signals maturity.

Question 3

How do you approach API design for maintainability?

Accepted Answer

Talk about designing for the consumer: consistent naming and error shapes, versioning strategy, pagination from day one, and backwards compatibility as a hard constraint. A concrete story about evolving an API without breaking clients — expand-then-contract — lands better than listing REST conventions.

Question 4

Tell me about a time you improved the reliability of a backend system.

Accepted Answer

Quantify reliability before and after (error rate, uptime, pager volume) and name the mechanism: retries with backoff, idempotency keys, circuit breakers, better health checks, or killing a flaky dependency. Show you treat reliability as a feature with a budget, not an afterthought.

Question 5

How do you balance building new features with maintaining existing systems?

Accepted Answer

Describe an explicit allocation — many teams reserve a fixed share of each sprint for maintenance — and how you make invisible work visible with a tech-debt register tied to incidents and slowdowns it causes. Show you can argue the business case for paying debt down, not just complain about it.

Question 6

Design a rate-limiting system for an API.

Accepted Answer

Clarify requirements first (per-user or per-IP, hard block or throttle), then compare algorithms — token bucket allows bursts, sliding window is smoother — and where the counters live: in-process for one node, Redis with atomic operations for a fleet. Cover the details that show production experience: 429s with Retry-After, and fail-open vs. fail-closed when the limiter itself is down.

Question 7

How would you handle database migrations with zero downtime?

Accepted Answer

Describe expand-then-contract: add the new column or table, dual-write, backfill in batches, switch reads, then drop the old path in a later release. Key points interviewers listen for: every step is individually reversible, deploys and migrations are decoupled, and long-running backfills are throttled to protect production traffic.

Question 8

Explain the CAP theorem and its implications for distributed systems.

Accepted Answer

State it precisely — during a network partition you must choose between consistency and availability; when there's no partition you don't sacrifice either. Then make it concrete: a payment ledger picks consistency, a product-view counter picks availability, and most real systems make this choice per-operation, not per-database. Mentioning tunable consistency shows depth.

Question 9

How do you approach caching? When would you use Redis vs. CDN?

Accepted Answer

Frame it as: cache at the layer closest to the user that correctness allows. CDN for static and public content at the edge; Redis for dynamic, per-user, or computed data your app reads repeatedly. Then address the hard parts — invalidation strategy, TTLs, cache stampede protection — because 'add Redis' is easy and staleness bugs are not.

Question 10

Design an event-driven architecture for an e-commerce order processing system.

Accepted Answer

Sketch the flow: order service emits events to a durable log (Kafka/queue), and independent consumers handle payment, inventory, and notifications. Then hit the distributed-systems realities that separate senior answers: idempotent consumers (events deliver at-least-once), the outbox pattern to atomically persist-and-publish, dead-letter queues, and sagas for multi-step failures like payment declined after inventory was reserved.

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