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Transform a time series for a supervised learning model?
This tests your ability to reframe a time series problem for tabular models. A great answer explains creating features from lags, rolling windows, and calendar data, then emphasizes using a time-aware validation split. A red flag is forgetting validation.
Design a multi-touch attribution model
Pick a model (time-decay, position-based, or data-driven Shapley), stitch touchpoints by user identity into ordered paths, then assign fractional credit.
Propose a multi-touch attribution model and its data pipeline
This tests your ability to choose a practical data model under real-world constraints. Propose a time-decay or position-based model, then describe the data pipeline: event collection, identity resolution, and aggregation. A red flag is ignoring signal loss.
Propose a multi-touch attribution model and its data pipeline
Tests your grasp of attribution models and their data engineering needs. Propose a rule-based model (e.g., time-decay), outline the data pipeline for it, and acknowledge privacy-driven signal loss. A red flag is ignoring the challenge of identity resolution.
Implement CDC from OLTP to warehouse
Contrast log-based CDC with query-based timestamp polling, cover deletes and load on source, then pick log-based for minimal impact.
Implement CDC from an OLTP database to a data warehouse
This tests your grasp of production system trade-offs. A good answer compares log-based and trigger-based CDC, focusing on source impact and data fidelity, then recommends log-based for its low overhead.
How would you implement Change Data Capture (CDC)?
Tests your grasp of data replication trade-offs. A great answer compares log-based CDC (low impact, complete) with query-based methods (higher impact, misses deletes), and recommends log-based CDC for its minimal production impact.
Define a consistent day across timezones
Store events in UTC, capture the local/source timezone, then convert to a single reporting timezone at query time.
How do you handle timezones for a global daily sales report?
This tests your ability to translate ambiguous business needs (a "day") into a robust data model. First, clarify the business definition of a day. Then, store all event times in UTC and convert to the target timezone at query time for reporting.
How do you handle timezones for a daily global sales report?
This tests your understanding of time data modeling and business requirements. A good answer stores events in UTC with a timezone identifier, then converts to the business's chosen 'day' at query time. A red flag is storing local time without context.
Interpret a p-value in an A/B test
Define it as P(data this extreme | null true), interpret 0.03 against a 0.05 threshold, and state what it is NOT.
What is a p-value, and what does 0.03 practically mean?
This tests your ability to translate stats into business decisions. A great answer defines p-value, compares 0.03 to the standard 0.05 threshold to reject the null hypothesis, and recommends shipping.
What does a p-value of 0.03 mean in an A/B test?
This tests your practical grasp of statistical significance. A good answer defines p-value (probability of the result if the null hypothesis is true), explains that p=0.03 is significant vs. alpha=0.05, and concludes you can reject the null.
Calculate MRR with SQL including annual plans
Sum monthly_price for subscriptions active this month, filter on start and end dates, and normalize annual plans by dividing annual price by 12.
Calculate Monthly Recurring Revenue (MRR) with SQL
This tests your ability to translate a business metric into a robust SQL query, handling time-series logic. A good answer filters for active subscriptions, sums the price, and correctly amortizes annual plans. A red flag is using incorrect date filtering.
Calculate MRR from a subscriptions table using SQL
Tests your ability to translate a business metric (MRR) into a precise SQL query. A great answer filters for active subscriptions this month and sums their prices, correctly amortizing annual plans.
Design analytics event schema validation
Define a schema registry, validate at both client (fast feedback) and server (authoritative gate), and quarantine failures to a dead-letter store.
Design a Schema Validation System for Analytics Events
Tests your grasp of data quality engineering, client/server trade-offs, and failure design. A good answer defines a Tracking Plan, enforces it on both client and server, and handles failures by blocking or forwarding with violation flags.
Design a Schema Validation System for Analytics Events
This tests your ability to balance data quality, performance, and developer experience. A good answer defines a central 'Tracking Plan,' enforces it on the client for feedback and the server for integrity, and quarantines failed events.
Determine A/B test sample size
Define baseline rate, minimum detectable effect, significance (alpha), and power (1-beta); smaller effects and stricter thresholds need more users.