Data and Technical Strategy Alignment Questions
Assess how the candidates technical experience and perspective align with the companys data strategy, infrastructure, and product architecture. Candidates should demonstrate knowledge of the companys scale, data driven products, and technical tradeoffs, and then explain concretely how their past work, tools, and approaches would support the companys data objectives. Good answers connect specific technical skills and project outcomes to the companys announced or inferred data and engineering priorities.
EasyTechnical
0 practiced
Explain the differences between a data warehouse, a data lake, and a lakehouse in the context of supporting ML model development and production. For an AI team that needs fast analytical queries, reproducible model training, and cost-effective storage for raw telemetry, recommend an architecture and justify it.
EasyTechnical
0 practiced
Given this simple event table schema for a web product, write an SQL query (ANSI SQL) to compute weekly active users (WAU) per product category for the last 8 weeks. Schema:Return rows: week_start, category, wau. Describe assumptions about timezones and late-arriving events.
events(event_id bigint, user_id bigint, product_id bigint, category varchar, event_ts timestamp)MediumSystem Design
0 practiced
Design an incremental model retraining pipeline that triggers retraining when data drift is detected. Describe drift detection metrics, thresholds, retraining orchestration, validation steps, and how you'd safely deploy retrained models with canarying and rollback.
MediumTechnical
0 practiced
Discuss cost and performance trade-offs when deciding to store raw telemetry in hot S3-tier vs archiving to cold storage, and whether to materialize precomputed features or compute them on demand. Provide an example calculation approach for estimating monthly cost vs latency.
MediumTechnical
0 practiced
Describe a monitoring and observability plan for production data pipelines that feed models. List key metrics, SLOs/SLA examples, alerting strategy, and tools you'd integrate to detect pipeline regressions that could cause model-quality degradation.
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