Technology Selection & Deep Technical Knowledge Questions
Deep understanding of specific technologies relevant to complex system design. Master databases (PostgreSQL, Cassandra, DynamoDB, Elasticsearch), message queues (Kafka, RabbitMQ), caching systems (Redis), search engines, and frameworks. Understand their strengths, weaknesses, trade-offs, operational characteristics, scaling patterns, and common pitfalls. Be able to justify technology choices based on specific system requirements.
MediumTechnical
0 practiced
Design a DynamoDB schema for an orders system where queries include: (A) get orders by order_id, (B) list orders for a user sorted by created_at, and (C) find recent high-value orders across users. Show partition key and sort key choices, and propose GSIs or LSIs to support these patterns. Explain trade-offs and how to handle hot partitions.
EasyTechnical
0 practiced
Compare Kafka and RabbitMQ from a data-science pipeline perspective. For a high-throughput event-ingest pipeline that needs fan-out to multiple consumers, fault-tolerance, and message replay for rebuilding state, which would you choose and why? Discuss ordering guarantees, durability, consumer models, and operational complexity.
HardSystem Design
0 practiced
Design a real-time recommendation system that serves personalized item recommendations with <50ms latency at 50k QPS. Describe the full stack: ingestion, feature computation (streaming vs batch), feature storage and serving (online store and caches), ranking model serving (ensemble, candidate generation, rerank), and technology choices (Kafka/Flink, Redis/Cassandra, model servers). Justify each choice with scaling and operational trade-offs.
HardSystem Design
0 practiced
Design an architecture for performing large-scale feature backfills and model retraining: include orchestrator (Airflow/Argo), compute (Spark/Beam), storage layout (versioned parquet/Delta), idempotency and checkpointing, and how to track and store feature versions to ensure experiment reproducibility. Address cost control and incremental vs full backfills.
EasyTechnical
0 practiced
List common ways data scientists use Redis in production. For each use case (caching feature values, online feature store, session store, Redis Streams for lightweight streaming), describe trade-offs in consistency, latency, memory footprint, and operational maintenance.
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