Advanced Real World Problem Solving Questions

Evaluate the candidates ability to solve complex multi layered technical and design problems by making reasonable assumptions, articulating trade offs, and handling edge cases. Candidates should show how to decompose problems that span networking caching persistence and performance optimization, select architectures and algorithms with explicit trade off analysis such as speed versus simplicity and functionality versus performance, and consider failure modes including network failures device limitations and concurrent access patterns. Strong responses include clear assumption statements, alternative approaches, complexity and cost considerations, testing and validation strategies, and plans to monitor and mitigate operational risks.

MediumSystem Design

0 practiced

Describe a robust strategy for schema evolution in an event-driven microservices architecture (producers and multiple consumers). Explain use of a schema registry, compatibility checks (backward/forward), version negotiation, and operational processes to roll out breaking changes safely.

MediumSystem Design

0 practiced

Design a global read cache for analytics dashboards across multiple regions: 1M dashboards, 500k users, and a few thousand updates per minute. Describe cache placement (CDN, regional caches), invalidation mechanisms (pub/sub, CDC), TTL strategy, and how you'd minimize the inconsistency window while keeping cost reasonable.

MediumTechnical

0 practiced

A bulk update needs to update a user profile across email-service, search-index, and analytics pipelines. Design the failure-handling strategy: discuss the transactional-outbox pattern, saga-based compensations, ordering guarantees, and how to make the process observable and resumable by operators.

HardSystem Design

0 practiced

Design a reprocessing (backfill) system that allows safe replay of petabytes of historical events to recompute derived datasets without impacting live production SLAs. Address isolation (compute/storage), deduplication, testing/backfill dry-runs, and incremental cutover of consumers to new outputs.

HardTechnical

0 practiced

Design end-to-end exactly-once processing for a streaming pipeline that ingests events from external producers and writes aggregates to external sinks that do not natively support idempotent writes. Describe checkpoints, deduplication strategies, transactional sinks, or two-phase commit trade-offs and how you'd scale the dedupe state.

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