Data Organization and Infrastructure Challenges Questions

Demonstrate knowledge of the technical and operational problems faced by large scale data and machine learning teams, including data infrastructure scaling, data quality and governance, model deployment and monitoring in production, MLOps practices, technical debt, standardization across teams, balancing experimentation with reliability, and responsible artificial intelligence considerations. Discuss relevant tooling, architectures, monitoring strategies, trade offs between innovation and stability, and examples of how to operationalize models and data products at scale.

EasyTechnical

0 practiced

Differentiate observability and monitoring in the context of ML systems. Give examples of metrics or logs that serve observability purposes versus those used for simple alerting/monitoring.

MediumTechnical

0 practiced

Write a SQL query that computes 7-day rolling user retention from events(user_id, event_date). Your solution should handle users with missing activity days and explain assumptions about timezones and event deduplication.

MediumTechnical

0 practiced

Design a lightweight onboarding checklist and technical standards to encourage standardization across ML teams without stifling experimentation. Include standards for feature naming, experiment tracking, data catalogs, and deployment conventions.

HardSystem Design

0 practiced

Design a cost-aware feature store including storage tiering (hot/warm/cold), feature materialization strategies (precompute vs compute-on-demand), and autoscaling considerations for online serving while targeting 99.95% availability. Explain cost vs freshness trade-offs.

HardTechnical

0 practiced

Design metrics and instrumentation to detect upstream data pipeline issues that silently corrupt features (examples: timezone shift, unit changes, new nulls). Specify which invariants to track at feature-level and how to aggregate alerts to reduce noise.

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