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
Write Python code using scikit-learn and Optuna that performs hyperparameter tuning for a RandomForestClassifier. Include how you define the objective, run trials, and log the best hyperparameters to an experiment tracker (mention which tracker you use). Show parallelization approach if applicable.
EasyTechnical
0 practiced
Write a SQL query (assume PostgreSQL) to compute a rolling 7-day average transaction amount per user from a transactions table: transactions(transaction_id, user_id, amount numeric, occurred_at timestamp). Include how you handle days with no transactions for a user (carry forward last known average or use null).
EasyTechnical
0 practiced
Describe how you set up dependency and environment management for reproducible experiments in Python. Compare venv, conda, pipenv, and pip-tools in terms of isolation, cross-platform portability, binary dependencies (e.g., for TensorFlow), and CI integration. Provide an example workflow you would recommend for a small team.
EasyBehavioral
0 practiced
Describe a situation where you chose Python over R for a data science project. Include specific libraries you used (for example: pandas, scikit-learn, TensorFlow), why you selected them, how package management and environment reproducibility were handled, and how the language choice affected deployment and collaboration with engineering teams.
MediumTechnical
0 practiced
Explain strategies to use GPU instances efficiently for model training to minimize cost without compromising training quality. Discuss spot/preemptible instances, mixed-precision training, gradient accumulation for small batch sizes, data pipeline bottlenecks, and job scheduling considerations.
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