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.
MediumTechnical
0 practiced
Your ETL pipeline occasionally increases previously reported daily metrics due to late-arriving events. As PM, design a measurement approach and data SLA to account for late-arriving data while keeping stakeholders informed and minimizing confusion.
HardSystem Design
0 practiced
Your analytics event schema requires renaming a property and changing its type. Outline a schema migration strategy that allows queries to continue working, supports incremental backfills, minimizes downtime, and preserves continuity for metrics over time.
HardTechnical
0 practiced
You expect a small absolute increase of 0.5% in conversion from a proposed feature. Describe how to compute required sample size and experiment duration for 80% power and 5% significance, list required inputs (baseline conversion, alpha, beta), and discuss alternative strategies when required sample size is unattainable.
EasyTechnical
0 practiced
You inherit a product analytics dashboard but stakeholders say it does not answer key questions. List five practical improvements you would make to increase actionability and trust, such as adding definitions, context, filters, and freshness indicators, and explain why each helps decision making.
HardSystem Design
0 practiced
Design an analytics ETL pipeline for user events from mobile and web that ensures idempotent ingestion, handles late-arriving events and backfills, and provides daily materialized metrics with a freshness SLA of 6 hours. Describe core components, deduplication approach, watermarking strategy, schemas, and monitoring needs.
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