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.
MediumTechnical
0 practiced
Show how you would use Great Expectations to create an expectation suite for transactions ensuring: user_id is not null, amount > 0, and occurred_at is not in the future. Explain how to integrate running these checks inside an Airflow task and handling failed expectations.
EasyTechnical
0 practiced
Write a minimal Python example (using single quotes) showing how you would log parameters, metrics, and a model artifact to MLflow inside a training loop using scikit-learn. The example should include starting a run, logging a hyperparameter, one metric, and saving/logging the trained model as an artifact.
EasyTechnical
0 practiced
Compare MLflow and DVC and a model registry conceptually: describe which concerns each tool solves (experiment tracking, model artifact versioning, dataset/version lineage), and give a short example of how you might combine them in a reproducible pipeline.
MediumSystem Design
0 practiced
How would you instrument a production model inference service for monitoring? List key metrics you would capture (latency percentiles, error rates, feature distribution snapshots, model prediction confidence), the tools you might use for ingestion and visualization (Prometheus, Grafana, ELK), and propose an alerting strategy with example thresholds and remediation playbooks.
HardTechnical
0 practiced
Your company must ensure reproducibility and legal compliance for models trained on sensitive user data. Propose a tooling and process solution that provides data lineage, access control, anonymization/pseudonymization, and automated deletion workflows that integrate with CI pipelines and model registries, and explain trade-offs between utility and privacy.
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