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.

MediumTechnical

0 practiced

Explain why random k-fold cross-validation can lead to data leakage on time series forecasting tasks. Describe temporal cross-validation strategies (rolling-window, expanding window), how to set validation horizons aligned to business metrics, and how to estimate model degradation over time using backtesting.

EasyTechnical

0 practiced

Describe how you would detect trend, seasonality, and stationarity in a daily time series of conversion_rate. Name two decomposition or statistical tests (e.g., STL decomposition, Augmented Dickey-Fuller) and explain the actions you would take (differencing, deseasonalizing) if the series is non-stationary before fitting a forecasting model.

HardTechnical

0 practiced

A product analyst monitored an A/B test daily and stopped when p<0.05, then reported significance. Explain why this is problematic (optional stopping) and describe statistical methods that allow valid sequential monitoring: alpha-spending functions (O'Brien-Fleming, Pocock), sequential probability ratio test (SPRT), and Bayesian monitoring. Provide guidance on practical implementation and how to pre-register monitoring plans.

EasyTechnical

0 practiced

Database schema: events(event_id PK, user_id INT, event_time TIMESTAMP, event_type TEXT). Using Postgres SQL, write a query to compute Daily Active Users (DAU) for the last 30 days and a 7-day rolling average DAU. Return columns: date, dau, dau_7day_avg. Explain how you handle timezones and days with zero activity (missing dates).

HardSystem Design

0 practiced

Design a streaming anomaly detection system for key business metrics with near real-time ingestion and alerts. Describe architectural components (ingest, feature extraction, online detector, alerting), detection algorithms suited for streaming (EWMA, online STL residuals, streaming isolation forest), state management, strategies to avoid alert fatigue, and evaluation metrics to measure system effectiveness.

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