Data Problem Solving and Business Context Questions
Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.
MediumSystem Design
0 practiced
For a high volume events table (100M rows/day) in a warehouse like Snowflake/BigQuery/Redshift explain practical partitioning/cluster/sort key strategies to optimize queries that filter by date range and user_id. Discuss compaction, micro-partitions, partition pruning, and the impact on ingestion and storage cost.
MediumSystem Design
0 practiced
Design a conversion-funnel dashboard for signup -> activation -> paid conversion. For each step specify the metric definition, SQL logic to compute it, recommended visualizations to show drop-offs, what filters and cohorting to provide, and how to compute and display confidence intervals or statistical significance for observed changes.
MediumTechnical
0 practiced
You need to run exploratory analysis on a 1B row events table. Discuss pros and cons of sampling strategies (random, stratified, time-based), describe how sampling can introduce bias, and provide sample SQL approaches to implement stratified sampling to preserve key distributions.
HardTechnical
0 practiced
Given an A/B experiment that ran for 30 days, write SQL to compute cohort-based LTV per arm controlling for baseline covariates using propensity score weighting or matching. Describe how to account for censoring, recommend a method to compute confidence intervals for LTV differences, and list common pitfalls.
MediumTechnical
0 practiced
Finance and marketing disagree on the definition of 'monthly active user'. As the BI analyst, describe how you would mediate the conflict, propose a process to arrive at a canonical definition, and outline how to implement governance so the canonical metric is discoverable and used consistently across reports.
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