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

Seasonality is strong in your key metric and you want to run an online experiment. Describe concrete experimental design and analysis choices to account for seasonality and calendar effects (e.g., weekends, holidays). Include how to choose experiment duration, randomization strategy, blocking, and post-analysis adjustments.

MediumTechnical

0 practiced

Given tables orders(order_id, user_id, order_date) and order_items(order_item_id, order_id, item_price, quantity), write an SQL query that returns total revenue per user for a given month, ensuring you do not double-count by joining correctly. Explain common join mistakes that cause over-counting and how your query avoids them.

MediumTechnical

0 practiced

You are building anomaly detection for daily revenue for a global product. Describe an end-to-end approach: data preprocessing, a baseline statistical detection method (e.g., control charts), a machine-learning-based approach (e.g., isolation forest or Prophet residuals), alerting thresholds, and how you'd avoid false positives due to holidays or large campaigns. Include how you'd validate the detector and measure its precision/recall in production.

HardTechnical

0 practiced

High cardinality features like 'product_id' appear in your model. Discuss practical strategies for encoding and modeling these features for a tree-based model and a neural network: target encoding with smoothing, hashing, embeddings, frequency encoding. For each, explain advantages, leakage risks, and computational trade-offs in production.

EasyTechnical

0 practiced

Describe the core components of a basic A/B test for a new checkout flow: defining treatment and control, selecting primary and guardrail metrics, randomization unit, sample size considerations, duration, and stopping rules. What common pitfalls would you watch for during the experiment?

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