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.
EasyTechnical
50 practiced
Explain ACID vs BASE and common distributed consistency models (strong, eventual, causal, read-your-writes). From an SRE perspective, how do these models influence replication setup, failover decisions, client libraries, and runbook actions during network partitions? Map each model to real products (Postgres, Cassandra, DynamoDB) and give operational implications.
MediumTechnical
39 practiced
Your global application needs to sustain 10k writes/sec with single-digit millisecond tail latency and can tolerate eventual consistency. Compare self-managed Cassandra vs AWS DynamoDB for this workload. Discuss operational overhead, scaling patterns, cost, backup/restore, multi-region replication, and SRE responsibilities for each choice.
MediumSystem Design
80 practiced
Design a backup and restore strategy for a critical PostgreSQL cluster with RTO < 1 hour and RPO < 5 minutes. Include base backups, WAL archiving/PITR, replica promotion strategies, testing restores, retention policies, and automation for periodic restore verification.
HardTechnical
39 practiced
Explain patterns for implementing cross-service transactional integrity: two-phase commit (2PC) vs sagas (orchestrated/choreographed). From an SRE perspective, discuss operability, failure modes, monitoring, recovery strategies, and which pattern you would recommend for long-running distributed business transactions.
HardSystem Design
75 practiced
Design a system to enforce GDPR Right-to-Be-Forgotten across heterogeneous storage (Postgres, S3, Elasticsearch, Kafka). Include deletion propagation mechanisms, verification and audit logging, soft-delete vs hard-delete strategies, how to handle backups and immutable logs, and procedures to demonstrate compliance.
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