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Application Programming Interface Design and Scalability Questions

Designing application programming interfaces that remain reliable, performant, and maintainable at high scale. Candidates should understand how interface decisions affect scalability, availability, latency, and operational complexity and be able to reason about trade offs between client complexity and server responsibility. Core areas include stateless interface design, pagination and cursor strategies for large result sets, filtering and search optimization, payload minimization, batching and streaming, and techniques to reduce server load while preserving client experience. Resilience and operational controls include rate limiting and quota management, throttling, backpressure and flow control, retry semantics and idempotency patterns, error format design and explicit identification of retryable errors, and strategies for graceful degradation under overload. Evolution and compatibility topics include backward compatible versioning strategies, deprecation policies, contract design and testing approaches to avoid breaking consumers. Infrastructure and deployment considerations include API gateway and edge patterns, interaction with load balancers and traffic distribution, caching and content delivery, routing fault tolerance, health checks and canary rollout strategies, and observability through metrics, distributed tracing, and logging to support capacity planning and incident response. Security considerations such as scalable authentication and authorization, credential and key management, and permission models are also important. Candidates should be prepared to discuss concrete patterns, trade offs, algorithms, and operational playbooks for designing and running high traffic application programming interfaces.

HardSystem Design
0 practiced
Design end-to-end observability for a request that traverses 50 microservices: include tracing design, sampling strategy, correlation IDs, metrics to capture at each hop, log context, and tools or formats to reconstruct user-facing latency hotspots during incidents. Explain how to keep tracing overhead low while enabling effective root-cause analysis.
MediumTechnical
0 practiced
Given table posts(id BIGINT PRIMARY KEY, created_at TIMESTAMP, title TEXT), write a SQL query implementing keyset pagination to return the next 50 posts after a given cursor (last_created_at, last_id). Explain which indexes you would use and how to handle ties when created_at is not unique.
HardTechnical
0 practiced
Propose a schema evolution strategy for real-time streaming APIs (for example Kafka topics or HTTP event streams) consumed by many independent consumers who may lag. Describe use of schema registries, compatibility rules, transformation layers (e.g., Kafka Streams or a gateway), and how to support forward and backward compatibility while allowing safe additive changes.
MediumTechnical
0 practiced
A partner sends bursts of webhooks that temporarily overwhelm your consumers. Propose a robust webhook ingestion design including acceptor queues, ack/nack semantics, retry scheduling, rate-limiting feedback to partners, batching options, and how to achieve at-least-once delivery with idempotent handling.
MediumTechnical
0 practiced
Explain how backpressure and flow control operate in HTTP/2 and gRPC streaming. Discuss transport-level windowing, application-level flow control, how servers or clients can signal willingness to accept data, and techniques like credit-based flow control or batching to avoid buffer bloat and out-of-memory conditions.

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