Database Selection and Trade Offs Questions

How to evaluate and choose data storage systems and architectures based on workload characteristics and business constraints. Coverage includes differences between relational and nonrelational families such as document stores, key value stores, wide column stores, graph databases, time series databases, and search engines; mapping query patterns and latency requirements to storage options; trade offs between strong consistency and eventual consistency and their impact on availability and complexity; partition key design, replication strategies, and high availability considerations; operational concerns including backups, monitoring, vendor and cost trade offs, migration or hybrid strategies, and when to adopt polyglot persistence. Senior level discussion includes selecting specific managed services and reasoning about expected load patterns, failure modes, and operational burden.

HardTechnical

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For model training that requires deterministic datasets, explain trade-offs between enforcing strong consistency at ingestion versus designing pipelines that are idempotent and tolerant of eventual consistency. Provide concrete patterns (snapshots, watermarking, CDC with ordering) to ensure deterministic training sets.

HardTechnical

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Compare failure modes and backup/restore strategies for managed NoSQL (DynamoDB), managed SQL (Cloud SQL), and object-store-backed data lakes (S3 + Glue). Define RPO/RTO planning, backup cadence, cross-region replication, and describe how you would test restores for each system.

MediumTechnical

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Design monitoring, observability, and alerting for a production feature store used for online model serving. Include key SLIs/SLOs, metrics to track (latency, freshness, error rates), dashboards, alert thresholds, and an incident runbook for degraded performance or data drift.

EasyTechnical

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Describe polyglot persistence and provide three concrete examples where you'd combine different stores (e.g., document DB + search engine, graph DB + key-value store) for a data product. For each example, map the query patterns to the chosen stores and explain synchronization concerns.

MediumTechnical

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Tell me about a time you convinced stakeholders to change a database choice or architecture for a data product. Use the STAR method: situation, task, action, result. What metrics did you present to justify the change and how did you measure success afterwards?

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