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
103 practiced
You are given a dataset intended for a monthly finance report. It contains duplicate invoice rows, missing customer IDs, and inconsistent date formats. Describe a concrete step-by-step plan (including specific tools: SQL, Python, Excel) to deduplicate, impute or escalate missing values, normalize dates, and document the cleaning decisions so downstream users trust the source.
EasyTechnical
62 practiced
How do you use version control (Git) effectively when collaborating on Jupyter notebooks or analytic scripts? Describe strategies to avoid noisy diffs for notebooks, how to structure repositories for analyses, and how to handle binary outputs or large datasets in the repo.
MediumTechnical
61 practiced
You are onboarding dbt for a retail analytics project. Describe a recommended dbt project structure: staging models, intermediate marts, final marts, sources, seeds, tests, and docs. Explain when to use incremental models vs full-refresh, how to use snapshots, and examples of tests you'd write for critical metrics.
EasyTechnical
71 practiced
What are the core visualization best practices you follow when creating dashboards for non-technical executives? Cover chart type selection, color usage (including color-blind accessibility), labeling/annotations, simplifying views, avoiding misleading axes, and when to use tables versus charts.
EasyTechnical
62 practiced
Compare Tableau and Power BI for a mid-sized e-commerce company that stores data in Snowflake and has about 10 analysts. Discuss trade-offs around licensing/cost, deployment (cloud vs on-prem), integration with the data warehouse, learning curve for analysts, advanced analytics (R/Python integration), governance/certified datasets, and recommended scenarios when you'd choose one over the other.
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