Straightforward enough that I almost overthought it.
Start by clarifying the schema and what 'visible days' means (e.g., days with impressions or non-null visibility metrics). Then write a query that groups by shop, applies a HAVING clause to filter out shops with zero visible days, and orders the results descending by total visible days.
Pro tip: Mention that you would validate the metric definition with stakeholders and consider edge cases like time zones or partial days, showing you think beyond just writing SQL.
Ask clarifying questions about what 'visible days' means (e.g., days with at least one impression, or sum of daily visibility flags) and confirm the table structure and join keys.
Determine whether to count distinct dates or sum a daily visibility metric per shop, ensuring the correct level of granularity.
Construct the query: SELECT shop_id, SUM(visible_days) AS total_visible_days FROM ... GROUP BY shop_id HAVING total_visible_days > 0 ORDER BY total_visible_days DESC.
Check for edge cases (e.g., shops with no data, date ranges) and consider indexing or partitioning for performance if the dataset is large.
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 the goal of the visibility tiers, then walk through the SQL logic step-by-step: first join the shop category table to the previous query, then apply CASE to bucket shops based on a chosen metric (e.g., GMV, views). Emphasize that the bucketing thresholds should be data-driven and aligned with business definitions of visibility.
Pro tip: Mention that you would validate the bucketing by checking the distribution of shops across tiers and ensuring the thresholds are meaningful (e.g., not too many shops in one bucket). Also, consider using window functions or percentile-based thresholds to make the tiers dynamic and robust to outliers.
Ask about the shop category table structure, the metric to define visibility (e.g., impressions, sales), and the desired number of tiers. Confirm the join key (e.g., shop_id) and any filters.
Extend the previous query by joining the shop category table on shop_id, ensuring you handle potential duplicates or missing categories appropriately (e.g., LEFT JOIN).
Write a CASE statement that buckets shops into visibility tiers based on the chosen metric. Use clear, business-relevant thresholds (e.g., top 10% = 'High', next 30% = 'Medium', rest = 'Low').
Check the distribution of shops across tiers, ensure no unexpected NULLs, and consider if thresholds should be dynamic (e.g., using percentiles) or static. Adjust as needed.
Discuss how these tiers can be used for product analytics, such as identifying low-visibility shops for interventions or measuring the impact of visibility on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
RANK() OVER (PARTITION BY category ORDER BY avg_visibility DESC) and I got there, but I initially wrote DENSE_RANK and had to backtrack when they asked about ties.
First, compute the average daily visibility per shop per category using a GROUP BY on shop and category. Then, apply a window function like RANK() or DENSE_RANK() over a partition by category and order by the average visibility descending to rank shops within each category. Finally, select the shop, category, average visibility, and rank.
Pro tip: Clarify whether ties should receive the same rank (using RANK or DENSE_RANK) or sequential ranks (ROW_NUMBER), and mention that the choice depends on business requirements. Also, consider if you need to handle days with no visibility data (e.g., treat as zero or exclude).
Identify the tables containing shop visibility data, including columns for shop ID, category, date, and visibility metric. Clarify what 'average daily visibility' means (e.g., average over a time period).
Use a GROUP BY on shop and category to calculate the average visibility. If daily visibility is already aggregated, you may need to average across days.
Use RANK() or DENSE_RANK() with PARTITION BY category ORDER BY average_visibility DESC to assign ranks. Choose the appropriate function based on tie-handling requirements.
Output shop, category, average visibility, and rank. Order by category and rank for readability.
Check for ties, missing data, and whether the ranking should be based on all shops or only those with sufficient data. Discuss how to handle these scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition: activity rate as the proportion of users active on a given day within each tenure bucket. Then, outline the SQL implementation using a snapshot date to compute tenure and aggregate activity, ensuring proper handling of time zones and active user criteria.
Pro tip: Consider using a rolling 7-day active definition to smooth out daily fluctuations and better capture user engagement, especially for new users who may have sporadic activity. Also, be explicit about how you handle users who signed up on the snapshot date (tenure = 0).
Clarify that activity rate is the number of active users divided by total users in each tenure bucket on the snapshot date. Define tenure buckets (e.g., 0-7 days, 8-30 days, 31-90 days, 90+ days) based on days since signup.
Specify the activity criteria (e.g., logged in, performed a key action) and the snapshot date (today). Ensure you have a table with user signup dates and a table with daily activity events.
Calculate tenure as the difference between the snapshot date and signup date. Use a CASE statement to assign each user to a tenure bucket.
Join user data with activity data for the snapshot date, flag active users, then group by tenure bucket to count total users and active users. Compute activity rate as active users divided by total users.
Check for edge cases (e.g., users with no activity, future signup dates) and ensure the query is efficient. Present the results in a clear table with tenure buckets and activity rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.