Infrastructure and Database Systems Questions

Fundamental infrastructure and database engineering concepts relevant to analytics platforms and general backend systems. Topics include relational and non relational database architecture indexing strategies query optimization replication and consistency trade offs sharding and partitioning approaches caching systems design message queues and event streaming systems and how these components integrate to meet performance reliability and cost objectives. Candidates should be able to reason about capacity planning high availability disaster recovery backup strategies and operational concerns such as monitoring alerting and graceful degradation under load.

HardTechnical

0 practiced

Implement a simple Python coordinator (pseudocode is fine) that moves a contiguous key range from source shard A to target shard B in a distributed key-value store. Focus on steps ensuring atomic handoff: lock range, copy data in chunks with checksums, switch routing atomically, drain in-flight writes, and provide idempotent retry semantics.

EasyBehavioral

0 practiced

Tell me about a time you led incident response for a database outage. Use the STAR format: describe the Situation, the Task you owned, the Actions you took to restore service and communicate, the Results including metrics (MTTR), and what permanent remediation you implemented to prevent recurrence.

EasyTechnical

0 practiced

List and justify essential monitoring metrics you would instrument for a production PostgreSQL primary and its read replicas. Include metrics for performance (latency, slow queries), replication health (lag), capacity (connections, disk usage), errors, and any custom analytics-oriented signals you would add.

EasyTechnical

0 practiced

Write a Python 3 script that reads a newline-delimited JSON logfile events.jsonl where each line is like {"timestamp": "2025-11-30T12:34:56Z", "level": "ERROR", "message": "..."}. Output per-minute counts of ERROR events for the last 24 hours to stdout. The script should be memory-efficient and handle malformed lines gracefully.

MediumTechnical

0 practiced

You must plan capacity for a new analytics cluster that will process 1PB of raw data per month. Outline the capacity planning steps and calculations you would perform: raw storage, replication overhead, compaction and compressed storage ratios, compute for ingestion and analytical queries, network egress, backup retention and restore capacity, and safety headroom assumptions.

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