Tool and Framework Expertise Questions
Focuses on hands on, production level experience with specific tools, libraries, and frameworks. Candidates should discuss concrete use cases where they applied tools, why they selected them, design and implementation details, performance and scaling considerations, maintainability, and lessons learned. This includes programming languages, data tooling, machine learning frameworks, testing frameworks, visualization tools, and infrastructure tools. Senior candidates should also explain how they evaluate and choose tools, integrate them into pipelines, and teach best practices to teams.
MediumSystem Design
28 practiced
Design a metadata and lineage schema to track feature provenance across ingestion, transformation, feature store, and model training. Specify the metadata fields you would capture, how you'd record transformations, and which tools or open standards (OpenLineage, Apache Atlas) you would use to implement this in a production environment.
HardSystem Design
42 practiced
Architect a multi-team, multi-region ML platform that supports: 10k inference requests per second, 1,000 concurrent training jobs, experiment tracking, a feature store, and model serving across regions. Provide high-level components, storage choices, CI/CD approach, security considerations, and measures to avoid vendor lock-in.
EasyTechnical
31 practiced
Describe the purpose of unit tests in a data science codebase. Provide an example pytest test for a preprocessing function that imputes missing values and ensures deterministic output for a given seed. Explain how you would integrate this test into CI.
MediumSystem Design
25 practiced
Design an architecture to serve two different ML models behind a single API with support for canary rollouts of new model versions. Use Kubernetes for deployment and a service mesh (Istio or Envoy) for traffic routing; explain health checks, gradual traffic shifting, and observability requirements to validate canary performance before full promotion.
HardTechnical
27 practiced
Compare deploying a recommendation model as a serverless function (e.g., AWS Lambda) versus a dedicated microservice on Kubernetes. Focus on cold starts, maximum model size, latency tails, cost at scale, stateful requirements, and operational trade-offs. Conclude with which approach you would pick for a low-latency, high-throughput recommender and why.
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