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.
MediumTechnical
0 practiced
An analytics request asks for an A/B test analysis. Outline how you would perform the test using R: data ingestion, cleaning, choosing test statistic, computing p-values and confidence intervals, and an approach to multiple comparisons correction if there are several metrics. Also discuss pre-registration and sample size calculation briefly.
HardTechnical
0 practiced
Design a monitoring and alerting strategy for data drift in input features used by downstream dashboards and ML models. Define what drift metrics to track, thresholds for alerts, how to surface drift to analysts, and automated remediation options such as retraining or data tagging.
HardSystem Design
0 practiced
Design an archive and cold storage strategy for a data warehouse that retains granular event data for five years but must keep query costs low for historical reporting. Explain partitioning, clustering, use of cheaper storage tiers, and how to provide occasional access to archived data without affecting performance of current dashboards.
EasyTechnical
0 practiced
You have an events table that records user interactions with columns: event_id, user_id, event_type, occurred_at (timestamp). Write an ANSI SQL query to compute monthly active users (MAU) for each month over the past 12 months. MAU is defined as the count of distinct user_id with at least one event in that calendar month. Return columns: month (YYYY-MM), mau. Assume database supports DATE_TRUNC or equivalent. Explain any assumptions about timezones and nulls.
HardTechnical
0 practiced
A critical executive dashboard has grown slow after additional cards were added. You can control the dataset, SQL models, and dashboard design. Describe an end-to-end approach to reduce rendering time from 3 minutes to under 15 seconds: include profiling steps, SQL refactoring, creation of aggregated tables, caching strategies, and dashboard-level UX compromises.
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