Data Analysis and Insight Generation Questions

Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.

EasyTechnical

95 practiced

You're asked to create a one-page dashboard for product managers showing top-level health: DAU, conversion rate, revenue, churn rate, and NPS trend. What charts and layout would you choose? Explain why each visualization and filter (e.g., date range, segment) is important for quick decision-making.

MediumTechnical

91 practiced

Write an SQL query (ANSI SQL) to compute 7-day rolling retention rate for new users. Use table users_events(user_id, event_name, occurred_at) and assume 'signup' and 'return' events. Return cohort_date, day_number (0-6), retention_rate.Explain assumptions about defining 'return'.

MediumTechnical

45 practiced

Design an experiment to test whether personalized onboarding increases 30-day retention. Specify population, randomization unit, sample size considerations (high-level), primary metric, guardrail metrics, and how you'd roll out based on results.

MediumTechnical

52 practiced

You built a dashboard that shows multiple correlated metrics. A product owner interprets a spike in one metric as causal. As a PM, how would you investigate whether the relationship is causal or correlational? Describe steps, analyses, and potential experiments.

MediumTechnical

62 practiced

You ran an A/B test on a pricing change. Variant B shows a 4% higher conversion but a 2% lower average order value (AOV). Explain how you would evaluate whether the pricing change is beneficial to revenue and long-term business health. What calculations and additional analyses would you perform?

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