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
0 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.
EasyTechnical
0 practiced
Define SLO, SLA, and error budget specifically for a database-backed service. How should SLOs and error budgets influence operational decisions such as replication factor, consistency level, automated failovers, and prioritizing reliability engineering work versus feature delivery?
HardSystem Design
0 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.
HardTechnical
0 practiced
A team plans to store aggregated metrics and analytics in Elasticsearch but requires strict numerical accuracy for long-term aggregations. Evaluate the suitability of Elasticsearch for these requirements and propose alternatives (time-series DBs like Prometheus/InfluxDB, columnar OLAP stores like ClickHouse) with trade-offs in query flexibility, storage efficiency, and operational complexity.
MediumTechnical
0 practiced
Design a safe, minimal-downtime migration plan to add a new non-null column with a default value to a very large PostgreSQL table used in production with many replicas. Include steps for schema change, backfill approach, application compatibility, write path handling, and rollback procedures.
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