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.

HardTechnical

47 practiced

A PostgreSQL cluster is showing high CPU and long-running autovacuum cycles causing IO spikes and slow queries. Describe a systematic debugging and remediation plan: what metrics to collect, SQL queries to run (pg_stat_activity, pg_stat_user_tables), autovacuum tuning knobs to adjust, and longer-term fixes to avoid repeated bloat.

MediumTechnical

52 practiced

Design an approach to deduplicate messages consumed from RabbitMQ where producers may retry and network blips cause duplicate deliveries. Discuss idempotent consumers, deduplication caches (TTL), and whether broker-side features can help.

MediumTechnical

49 practiced

Design a Cassandra data model for time-series metrics from IoT devices: 1M devices, each sending 1 write/minute. Requirements: efficient range queries by device+time, retention of 30 days, and hourly aggregation. Describe primary key, clustering order, compaction strategy, and tombstone handling.

MediumTechnical

77 practiced

Given schema:products(product_id PK, name text, category text, tags text[], price numeric, created_at timestamptz)

And queries:1) Find products by tag and sort by price asc2) Find recently added products in a category

Design PostgreSQL indexes to support both queries on 10M rows. Explain index types, multi-column vs expression indexes, and maintenance costs.

MediumTechnical

54 practiced

Write a PostgreSQL SQL statement to remove duplicate rows from table events(event_id, entity_id, occurred_at, payload) keeping only the latest row per entity_id by occurred_at. Use window functions and show the delete statement you'd run safely in production.

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