← Capital One Interview Insights
Pretty straightforward join between the summary table and the types table, multiply units by price and then divide each row's revenue by the total.
Start by clarifying the schema and assumptions (e.g., whether the summary table already has quantities or needs aggregation). Then write a SQL query that joins the summary to the pricing table, computes revenue per ticket type, and uses a window function to calculate each type's percentage of total revenue.
Pro tip: Mention that you'd validate the output by checking that the sum of revenue shares equals 100% and that total revenue matches a manual calculation. Also, note that you'd handle potential NULLs or missing prices with COALESCE or a LEFT JOIN to avoid silently dropping revenue.
Ask about the structure of the ticket sales summary (e.g., does it contain ticket_type and quantity_sold?) and the pricing table (e.g., ticket_type and price). Confirm whether quantities are pre-aggregated or need to be summed.
Join the sales summary to the pricing table on ticket_type, then calculate revenue as quantity_sold * price for each type. Use a CTE or subquery to keep the logic clean.
Use a window function like SUM(revenue) OVER () to get the overall total revenue without collapsing the per-type rows. Alternatively, use a separate aggregation and cross join, but window functions are more efficient.
Divide each type's revenue by the total revenue and multiply by 100 to get a percentage. Use ROUND to format to two decimal places, and handle division by zero if total revenue is zero.
Show the final query and explain how you'd validate it: check that shares sum to 100%, total revenue matches a manual sum, and consider edge cases like missing prices or zero sales.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was more conceptual than I expected in a SQL round.
Start by defining the components of total annual park entries: single-day tickets, five-day tickets, and annual passes. Express each component in terms of known per-unit entry assumptions and the unknown variable X, then sum them to form the total function.
Pro tip: Clearly state your assumptions about how annual pass entries are counted (e.g., each entry counts as one regardless of pass type) and consider edge cases like multi-day tickets being used by the same person. This shows analytical rigor and prevents ambiguity.
List all ticket types that contribute to total annual park entries: single-day tickets, five-day tickets, and annual passes.
Assign the number of entries per unit for single-day (1 entry) and five-day (5 entries) tickets based on the given assumptions.
Let X be the average number of entries per annual pass holder. Multiply X by the number of annual pass holders to get total entries from annual passes.
Add the entries from single-day tickets, five-day tickets, and annual passes to form the total annual entries function.
Combine like terms if possible and present the function clearly, ensuring all variables are defined.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got genuinely tricky.
Start by clarifying the definitions of 'active pass window' and 'qualifying visit', then outline a SQL-based solution that joins passes to visits on holder ID and visit timestamp within the pass period, aggregates visits per pass, and finally computes the average across all pass holders including those with zero visits. Emphasize handling multiple passes per holder by treating each pass period independently and using a left join to retain zero-visit holders.
Pro tip: Explicitly state your assumptions about edge cases (e.g., overlapping passes, visits on boundary dates) and propose a validation step, such as checking for duplicate pass periods or negative visit counts, to demonstrate production-ready thinking.
Define what constitutes an 'active pass window' (start/end dates inclusive?) and a 'qualifying visit' (e.g., any visit during the window, or only certain types). Confirm how to handle multiple passes per holder and overlapping periods.
Use a left join from the passes table to the visits table on holder ID and visit timestamp between pass start and end dates. This ensures pass holders with zero visits are included.
Group by pass ID (or holder ID and pass period) and count the number of qualifying visits. For zero-visit holders, the count will be zero due to the left join.
Calculate the average of the per-pass visit counts across all passes. If the metric is per holder (not per pass), first aggregate visits per holder across all their passes, then average.
Check for overlapping passes, visits exactly on boundaries, and duplicate records. Consider whether to deduplicate visits or passes, and validate results with sanity checks (e.g., total visits, number of holders).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mechanical once you have the 2.5 from the previous part.
First, recall the total entries formula you previously constructed, which likely multiplies the number of annual pass holders by the average visits per pass holder. Then, substitute the empirically computed average visits value into that formula and perform the multiplication to get the final number. Clearly state any assumptions about the number of pass holders and ensure units are consistent.
Pro tip: Always double-check that the average visits value is in the same time unit (e.g., annual) as the pass holder count, and mention that you would validate the result with a sanity check against known benchmarks or ranges.
Restate the formula you derived earlier, such as Total Entries = Number of Annual Pass Holders × Average Visits per Pass Holder.
State the computed average visits per annual pass holder from your analysis, ensuring it is clearly defined and sourced.
Plug the average visits value into the formula and perform the multiplication to obtain the final number of total entries.
Briefly assess whether the result is reasonable given the context, and explain what the final number represents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.