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.

HardSystem Design

0 practiced

Design a monitoring plan for a recommendation model in production. Include statistical performance metrics (e.g., AUC, calibration), business KPIs (CTR, retention), data-drift and feature-drift detectors, SLOs/SLIs, alert thresholds, and an automated remediation or rollback process. Explain how to prioritize alerts to reduce noise and how to tie observed model drift to business impact.

HardTechnical

0 practiced

You trained a model and SHAP shows feature A has high importance, but stakeholders suspect A is not causal but a proxy. Explain how to interpret SHAP/feature importance correctly, common pitfalls when features are correlated, and propose analyses (partial dependence, conditional permutation importance, causal diagrams, domain experiments) to investigate whether A is a proxy for another causal driver.

EasyTechnical

0 practiced

Explain in simple terms what a p-value is, the difference between Type I (false positive) and Type II (false negative) errors, and provide a concrete product-oriented example for each (what would a false positive or false negative look like for an A/B test measuring checkout conversion).

HardSystem Design

0 practiced

Describe designing experimentation at scale when features overlap and users may be in multiple active experiments (interference). Cover strategies including factorial design, cluster-randomization, orthogonal randomization, and causal corrections for interference. Discuss implications on sample size and analysis complexity.

EasyTechnical

0 practiced

Explain what cohort analysis is and how it differs from aggregate metrics. Given a table of user signups and purchases (user_id, signup_date, purchase_date), outline step-by-step how to compute a weekly cohort retention table (cohorts by signup week) and how to interpret different retention decay patterns (fast drop, steady tail).

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