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.
EasyTechnical
0 practiced
Define 'measurement window' in the context of BI metrics and explain why selecting an appropriate window matters for A/B tests, retention, and LTV. Provide examples showing how different window lengths (7, 30, 90 days) change interpretation and resulting business actions.
HardTechnical
0 practiced
Explain how to compute churn using survival analysis (Kaplan-Meier estimator) versus naive churn rate. Provide SQL or pseudocode to compute Kaplan-Meier on user activity data, explain how to handle censored observations (users with limited follow-up), and give an example interpretation where survival analysis offers deeper insight.
HardSystem Design
0 practiced
Define an observability and alerting plan to ensure the correctness of a revenue metric. List the signals (SLIs) to collect such as ingestion lag, schema drift, null-rate, high cardinality, and sampling rate. Design example SQL checks for each signal, propose dashboard widgets for metric health, alert thresholds, and a remediation runbook for a revenue metric breach.
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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