General Technical Tool Proficiency Questions

Familiarity and practical experience with technical productivity and analysis tools such as SQL, Python or R, data visualization platforms like Tableau and Power BI, Excel, and statistical or analytical software. Candidates should be able to describe depth of expertise, typical use cases, examples of real world applications, automation or scripting practices, and how they select tools for different problems. This topic includes discussing reproducible workflows, data preparation and cleaning, visualization best practices, and integration of tools into cross functional projects.

EasyTechnical

99 practiced

Explain what SQL window functions are and provide three concrete use cases a data scientist would use them for (ranking, running totals, and lag/lead calculations). Include a short SQL snippet that computes ROW_NUMBER() partitioned by user_id ordered by occurred_at.

EasyTechnical

76 practiced

You're delivering dashboards in Tableau or Power BI. Explain how you'd set up user access and row-level security so managers see only their region's data. Describe how this impacts data source configuration, extracts vs live connections, and dashboard performance.

HardSystem Design

76 practiced

You are responsible for implementing PII controls across analytics tools (SQL, Tableau, S3, Jupyter notebooks). Design policies and technical controls (masking, tokenization, RBAC, audit logs), developer workflows, and automation to detect and prevent PII leakage while enabling authorized analytics workflows.

MediumTechnical

74 practiced

You are handing a trained scikit-learn model to engineers for deployment. Describe how to package the model into a minimal Docker image for reproducible inference, covering model serialization (joblib/pickle), dependency pinning (requirements.txt or conda-lock), Dockerfile best practices for small images, environment variables, and a minimal Flask or FastAPI prediction endpoint.

HardTechnical

80 practiced

Model accuracy dropped by 12% over two weeks. Describe a triage plan to determine whether this is due to data drift, label drift, concept drift, feature engineering regressions, or an operational issue. List hypotheses to test, quick statistical checks and visualizations, logs and artifacts to examine, and immediate mitigation steps to reduce business impact.

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