A focused three-stage walkthrough
- 1Scope
Clarify scale, retention, custom aliases, and latency goals.
- 2Design
Propose the API and data model before drawing components.
- 3Stress-test
Address key generation, caching, hot links, replication, and failure modes.
Make every architecture choice traceable
Clarify users, core operations, scale, latency, consistency, retention, and what is explicitly out of scope.
Define APIs and data models, then connect components through the critical read and write paths.
Identify bottlenecks, failures, security concerns, and the trade-offs behind your scaling choices.
What a strong practice attempt includes
- Requirements drive the architecture
- APIs and data ownership are explicit
- Scale and failure modes are addressed
- Trade-offs are explained instead of hidden
Answer the main question first, then take these on one at a time:
- How would you prevent collisions?
- What becomes the bottleneck?
- How would analytics change the design?
Check your recording or written solution against this list:
- Starting with architecture before requirements
- Ignoring redirects as the critical read path
- Naming tools without trade-offs
This guide combines general interview-practice patterns with the public hiring material below. The practice prompt and coaching are PiriPiri AI editorial content, not official Amazon questions or answers.
Official sources reviewed 20 September 2026.