← Capital One Interview Insights
This one tripped me up more than it should have.
Start by clarifying the table schema and the definition of 'tier'. Then outline a two-level aggregation: compute median age per tier and the global median, and use COALESCE or a CASE expression to fill nulls with the tier median, falling back to the global median when the tier median is null. Finally, return customer_id and the imputed age.
Pro tip: Mention that you would validate the imputation by checking the distribution of imputed values and ensuring no tier with all nulls is left unfilled. Also note that using a window function like PERCENTILE_CONT with PARTITION BY tier can compute tier medians in a single pass, but you must handle the fallback carefully.
Clarify the table structure: customer_id, age, tier. Confirm that 'tier' is a column and that missing ages are represented as NULL. Ask if there are any edge cases, such as tiers with all NULL ages.
Use a window function or subquery to calculate the median age for each tier, ignoring NULLs. For example, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY age) OVER (PARTITION BY tier).
Calculate the median age across the entire table, ignoring NULLs. This will be used as a fallback when a tier has no non-null ages.
For each row, if age is NULL, replace it with the tier median if available; otherwise use the global median. Use COALESCE(tier_median, global_median) or a CASE expression.
Select customer_id and the imputed age (original age if not null, else the imputed value). Optionally, validate that no NULLs remain and that imputed values are reasonable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and business rules, then outline a step-by-step SQL solution using a UNION of both tables, a window function to rank rows by event_date, and a filter to keep the latest. Finally, discuss trade-offs and edge cases to demonstrate depth.
Pro tip: Mention that you would validate the upsert with a quick count check and consider idempotency to ensure the operation can be safely re-run without duplicating data.
Ask about the table structures, primary keys, and how to handle ties in event_date. Confirm that 'latest' means the most recent event_date and that all columns should be updated.
Use a UNION ALL to stack staging_events and events, ensuring column alignment. This creates a unified dataset for deduplication.
Apply a window function like ROW_NUMBER() OVER (PARTITION BY event_id ORDER BY event_date DESC) to assign a rank to each row within each event_id.
Select only rows where the rank equals 1, which gives the most recent version of each event. This handles both new inserts and updates.
Address performance implications (e.g., full table scan vs. incremental), tie-breaking rules (e.g., using another column), and how to handle deletions if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Stacking the imputed age logic from part A into this query is where things got messy for me live.
First, filter the events to the last 7 days and exclude non-monetary events, keeping only purchases and refunds. Then join with the customer age data (using imputed ages) and restrict to customers aged 18-65. Finally, aggregate net revenue (purchases positive, refunds negative) and distinct customer count per tier.
Pro tip: Clarify whether 'net revenue' should be calculated as the sum of purchase amounts minus refund amounts, and ensure that refunds are correctly attributed to the original purchase's tier if tier can change. Also, confirm that the 7-day window includes today and that 'today' is based on the event timestamp's timezone.
Restrict the event data to the 7-day window ending today and exclude non-monetary events like page views, keeping only purchases and refunds.
Join the filtered events with the customer table that contains imputed ages, ensuring you use the imputed ages from the first question.
Restrict the joined data to customers aged 18 to 65 inclusive.
For each tier, calculate net revenue by summing purchase amounts (positive) and refund amounts (negative), and count distinct customers who made at least one purchase or refund.
Return a table with tier, total net revenue for the window, and distinct customer count, ordered by tier or revenue as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The retention definition here is a bit unusual since it's looking backward at a prior window rather than forward, so I had to read it twice.
Start by clarifying the definitions of 'monetary event' and 'any event' and the tier dimension, then outline a SQL solution using two 7-day windows and a self-join or conditional aggregation. Finally, discuss indexing strategies and a testing plan to validate correctness.
Pro tip: Emphasize that the retention rate should be computed as the number of customers with a monetary event in the last 7 days who also had any event in the prior 7 days, divided by the total number of customers with a monetary event in the last 7 days, grouped by tier. Also, mention that using a calendar table or date spine can help handle missing dates and ensure accurate window calculations.
Confirm what constitutes a 'monetary event' and 'any event', the definition of 'tier', and the exact time windows (e.g., last 7 days from today, prior 7-day window).
Use conditional aggregation or a self-join to identify customers with a monetary event in the last 7 days and check if they had any event in the prior 7 days, then compute the fraction per tier.
Recommend indexes on (customer_id, event_date, event_type) and (tier, event_date) to speed up filtering and joins, and consider partitioning by date if the table is large.
Validate with edge cases (e.g., customers with events exactly on the boundary dates), compare against a manual calculation on a small sample, and check for NULLs or missing tiers.
Present the retention rate per tier and clearly state any assumptions made (e.g., event_date is a date not timestamp, tiers are mutually exclusive).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.