Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): cost, autoscaling, right-sizing, cloud
- Reliability: detect issues (monitoring) and limit blast radius (rollback, feature flags).
- Security: least privilege, secret rotation, supply chain.
- Automation: idempotency, repeatability, drift control.
- 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 "give-three-practical-ways-to-control-cloud-costs"
function explain() {
// Start from the core idea:
// Right-size instances based on metrics, use autoscaling for variable load, and buy reserved
}
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?