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

34 practiced

You need to produce a stratified sample of a very large dataset for quick model prototyping using PySpark. The dataset is too big to fit on the driver. Describe and show PySpark DataFrame code that performs a reproducible stratified sample by a categorical column `label` (imbalanced classes), preserving the relative class proportions and ensuring the sample size is approximately N rows.

MediumSystem Design

43 practiced

Design an ML observability system: list key metrics (e.g., prediction distribution, latency, input drift, feature importance changes), how to compute them efficiently in real-time and batch, retention policies, and how to wire alerts and runbooks. Sketch the minimal dashboard you would present to a model owner.

HardTechnical

33 practiced

As a staff-level engineer, design a policy/framework to let data scientists run experiments safely while protecting production systems. The policy should cover sandboxing, quotas, data access permissions, metrics isolation, and rollout pathways for successful experiments to production. Include enforcement mechanisms and incentives for compliance.

MediumTechnical

33 practiced

Describe design patterns and tools for versioning both models and the datasets used to train them. Compare DVC, MLflow, Delta Lake time-travel, and storing metadata + checksums in an artifact repository. Which approach do you prefer for a regulated industry and why?

MediumTechnical

36 practiced

Design a practical approach and tooling strategy to automatically detect, classify, and mask PII values in data pipelines across dev/test/prod environments. Include detection methods, masking strategies, key management, and how to allow safe access to derivatives for ML training.

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