Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): autoscaling, metrics, thrashing, ha
- Lifecycle: what happens at runtime (render/build, request/response, background jobs).
- Caching: where cache lives, cache keys, how to invalidate without chaos.
- Security: authn/authz, secrets, attack surface (SSRF/CSRF).
- Explain the "why", not just the "what" (intuition + consequences).
- Trade-offs: what you gain/lose (time, memory, complexity, risk).
- Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "what-is-autoscaling-and-what-is-a-common-pitfall"
function explain() {
// Start from the core idea:
// Autoscaling adjusts the number of instances based on demand (metrics like CPU, latency, qu
}
Common pitfalls
- Too generic: no concrete trade-offs or examples.
- Mixing average-case and worst-case (e.g., complexity).
- Ignoring constraints: memory, concurrency, network/disk costs.
Interview follow-ups
- When would you choose an alternative and why?
- What production issues show up and how do you diagnose them?
- How would you test edge cases?