Seemed straightforward but I kept second-guessing whether to filter on request_time or dropoff_time.
Start by clarifying the schema and definitions: what statuses indicate a completed trip, how to handle time zones, and whether 'last 7 days' means a rolling window or calendar days. Then write a SQL query that filters trips to the last 7 days, groups by city, and computes total requests, completed trips, and completion rate. Finally, order by completion rate ascending and discuss edge cases like cities with zero requests.
Pro tip: Mention that completion rate should be calculated as completed trips divided by total requests, and consider using NULLIF to avoid division by zero. Also, highlight the importance of aligning the time window with the business definition (e.g., last 7 full days vs. last 168 hours).
Ask clarifying questions about the status values (e.g., 'completed', 'cancelled'), the definition of 'last 7 days' (rolling vs. calendar), and time zone handling. Confirm that completion rate is completed/total requests.
Write a SQL query that filters trips where request_time is within the last 7 days, groups by city, and calculates total requests (COUNT(*)), completed trips (SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END)), and completion rate.
Use NULLIF or a CASE statement to avoid division by zero. Order the results by completion rate ascending. Consider whether to include cities with zero requests (likely exclude).
Sanity-check the output: ensure completion rates are between 0 and 1, and consider if any cities have unusually low or high rates. Discuss potential data quality issues (e.g., missing statuses).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and metric definitions, then outline a SQL query that filters completed trips in the last 7 days, computes average surge per driver per city per day for each time window, and joins the results to calculate the ratio. Finally, apply the conditions: both windows must have at least one trip and the ratio must exceed 1.5.
Pro tip: Mention that you would validate the surge multiplier field for outliers or missing values and consider whether the ratio should be weighted by trip volume or simply the ratio of averages, as this can significantly impact results.
Confirm definitions: completed trips, surge multiplier, time windows, and the 7-day period. Identify relevant tables (e.g., trips, drivers, cities) and ensure you understand how surge is recorded.
Write a subquery to select completed trips from the last 7 days, extract the date and hour, and classify each trip into the 20:00-21:59 or 14:00-15:59 window. Compute average surge per driver, city, and day for each window.
Join the two aggregated results on driver, city, and day, ensuring both windows have at least one trip. Calculate the ratio of the evening average surge to the afternoon average surge.
Filter to only rows where the ratio exceeds 1.5 and return the driver, city, day, and ratio. Consider ordering or limiting results as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Percentile_cont inside a window frame is one of those things I always have to look up.
Start by clarifying the schema and definitions (completed trips, wait time, city, date range). Then use a window function to compute the 95th percentile wait per city, filter out trips above that cutoff, and finally compute the median wait per city using another window function (e.g., PERCENTILE_CONT or NTILE).
Pro tip: Mention that percentile functions like PERCENTILE_CONT are analytic functions and can be used in a subquery or CTE to avoid self-joins, and that you'd validate the 95th percentile cutoff doesn't disproportionately remove data from small cities.
Confirm definitions: completed trips, pickup wait time, city, and last 7 days. Identify the relevant table and columns (e.g., trips with status, timestamps, city_id).
Use a window function like PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY wait_time) OVER (PARTITION BY city) to calculate the cutoff per city.
In a CTE or subquery, exclude trips where wait_time > city_95th_percentile. Ensure the filter is applied before median calculation.
Use another window function (e.g., PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY wait_time) OVER (PARTITION BY city)) on the filtered dataset to get the median.
Select distinct city and median wait. Discuss handling of ties, small sample sizes, and whether to round or report percentiles.
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 defining the time window (last 30 days). Then, for each evidence type (device ID and payment card hash), self-join the trips table on the evidence column to find rider pairs, ensuring rider_id1 < rider_id2 to avoid duplicates. Finally, union the results, deduplicate, and select the required columns including the earliest trip timestamp as first_seen.
Pro tip: Mention that you would handle potential NULLs in device_id or payment_card_hash by filtering them out before joining, and consider using a window function to get the first seen time efficiently. Also, discuss how to scale the query for large datasets, e.g., using partitioning or sampling if needed.
Confirm the table structure, column names, and definitions (e.g., rider_id, device_id, payment_card_hash, trip_date). Ensure the time window is correctly interpreted as the last 30 days from the current date.
Apply a WHERE clause to restrict the trips to those taken in the last 30 days, reducing the dataset size for subsequent joins.
Perform two separate self-joins: one on device_id and one on payment_card_hash, ensuring rider_id1 < rider_id2 to avoid duplicate pairs and self-pairs. Extract the evidence type and value from the join key.
Combine the results from both joins using UNION ALL, then deduplicate pairs that may appear multiple times (e.g., same pair sharing both device and card). For each pair and evidence type, compute the earliest trip timestamp as first_seen.
Select the final columns: rider_a, rider_b, evidence_type, evidence_value, first_seen. Validate that the output meets the requirements (e.g., no duplicate pairs, correct ordering).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pandas after four SQL questions felt like a gear shift.
First, filter trips to completed ones and compute each rider's first trip date. Then, for each rider, check if they have another completed trip within 7 days of their first trip, and aggregate by cohort date (first trip date) to calculate new riders, retained count, and retention rate.
Pro tip: Clarify the definition of 'within 7 days'—whether it's inclusive or exclusive—and confirm that retention is based on any trip after the first, not necessarily the second trip. Also, consider timezone and date boundaries.
Filter the trips DataFrame to only completed trips and ensure the date column is in datetime format. Optionally, filter to the given date range for first trips.
For each rider, find the minimum trip date (first completed trip). This defines the cohort date for each rider.
For each rider, check if there exists another completed trip within 7 days after their first trip date. Create a boolean flag for retention.
Group by cohort_date (first trip date) and compute the number of new riders (count of riders) and retained count (sum of retention flag). Calculate retention rate as retained count divided by new riders.
Return a DataFrame with columns: cohort_date, new_riders, retained_count, retention_rate. Ensure the date range filter is applied to cohort_date if specified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.