314,552 interview questions from 6,000+ companies.
Explain how you used a KPI and supporting metrics to diagnose a product issue and make a concrete product decision.
Explain how to distinguish early directional metrics from outcome metrics, using a clear KPI framework tied to product decisions.
Use customer feedback to identify the biggest pain points in the user journey.
Set a clear north star, supporting KPIs, leading indicators, and guardrails for a new product feature.
Approach for safely backfilling missing data while preserving correctness, idempotency, and data quality.
Explain what a p-value means in hypothesis testing and how it relates to statistical significance.
Explain what statistical significance means and why it matters when interpreting experimental or analytical results.
Explain SQL window functions and when to use ROW_NUMBER() versus DENSE_RANK() for ranked ticket analysis.
Practical approach for maintaining data quality across ML ETL pipelines, orchestration, and repeatable data processing.
Identify the most important user pain points using both qualitative and quantitative data.
Reason about sample size, power, and minimum detectable effect before launching an experiment.
Approach for maintaining high quality data across ML pipelines, from ingestion through feature generation and model consumption.
Explain how CTEs make complex PostgreSQL queries easier to read, debug, and maintain in reporting workflows.
Explain how to calculate cumulative totals in SQL using window functions, ordering, and optional pre-aggregation.
Explain how Excel-style pivot tables, aggregations, and financial calculations translate into SQL reporting workflows.
Explain how to validate SQL data before reporting, including null checks, duplicates, outliers, and aggregation reconciliation.
Define a North Star Metric for a product and explain how it guides KPI selection and growth decisions.
Explain how SQL supports analysis work through filtering, aggregation, and data preparation, and how it complements Excel and Tableau.
Explain how you used SQL aggregations and simple trend analysis to help a customer make a business decision.
Explain how SQL is used to extract business insights through filtering, aggregation, and trend analysis.
52 total questions
