実際の面接質問を、
企業別に。

Focus on encoding/decoding URLs, distributed databases, and scaling for high traffic.

Use a message queue like Kafka for scalability, implement retries with exponential backoff, and ensure delivery guarantees with at-least-once semantics.

Implement chunk-based file storage, use a distributed file system with data deduplication, and ensure fault tolerance with replication and eventual consistency.

Use a token bucket algorithm with Redis for distributed rate limiting, ensuring consistency across servers and low-latency performance.

Design with object-oriented principles, model parking spots and vehicles, and implement a pricing model based on duration and spot type.

Use WebSockets for real-time messaging, store messages in a distributed database like Cassandra, and scale with load balancers for concurrent users.

Implement a fan-out-on-write approach for feed generation, use caching with Redis for performance, and apply machine learning for ranking.

Use edge servers for low-latency content delivery, implement cache invalidation with TTL, and distribute load with global load balancers.

Use a relational database with locking for seat allocation, integrate a payment gateway, and handle concurrency with optimistic locking.