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
43 practiced
Explain data lineage and why metadata and provenance are critical for AI systems. Provide two examples where lineage directly affects model correctness, compliance, or the ability to roll back training data.
MediumTechnical
74 practiced
Explain the benefits and trade-offs of lakehouse formats (Delta Lake, Apache Iceberg, Apache Hudi) for ML feature storage and model training pipelines. For a pipeline that requires ACID updates, time travel, and performant scans, which would you choose and why?
MediumTechnical
47 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.
EasyTechnical
35 practiced
List and briefly describe the core components of a production ML data pipeline for a recommender system that supports offline training and online inference. Include ingestion, storage, transformation, feature materialization, model training, and serving components and how they interact.
MediumTechnical
36 practiced
Write PySpark Structured Streaming pseudocode (in Python) that deduplicates events in a stream using an event_id and watermark policy, emitting deduplicated events to a sink. Discuss trade-offs of state retention, watermark delay, and memory pressure.
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