Warmup question, basically just COUNT(*) FROM users.
Start by clarifying the exact requirement: do they want a simple count of all rows or a count of distinct users? Then write a straightforward SQL query using COUNT(*), and briefly explain your choice. Finally, mention any assumptions about the table schema and potential edge cases like NULLs or duplicates.
Pro tip: In a product analytics context, always consider whether 'total users' means all-time registered users or active users within a time frame; asking this shows you think beyond the syntax and understand business metrics.
Ask whether 'total users' means all rows in the users table or distinct users, and whether there are any filters like active status or date range.
Mention that you would check the table structure to identify the primary key or unique user identifier, and understand if there are duplicates or NULLs.
Use SELECT COUNT(*) FROM users; for total rows, or SELECT COUNT(DISTINCT user_id) FROM users; if duplicates are possible.
Walk through what the query does, discuss performance considerations (e.g., COUNT(*) vs COUNT(column)), and suggest testing on a sample if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
You need to extract the month from created_at, group by it, and sort descending with a LIMIT 1.
Clarify the definition of 'signup' and the relevant time period, then write a SQL query that groups signups by month and orders by count descending to find the top month. Finally, present the month and count, and discuss any data quality considerations or trends.
Pro tip: Always confirm whether 'signup' means account creation or first meaningful action, and check for timezone or duplicate account issues that could skew monthly counts.
Ask clarifying questions about the definition of 'signup', the time range, and whether to consider all users or a specific segment.
Identify the relevant tables and columns, such as a users table with a signup timestamp, and check for data quality issues like nulls or duplicates.
Use SQL to extract the month from the signup date, count signups per month, and order by count descending to get the top month.
Sanity-check the results, consider timezone adjustments, and interpret the finding in the context of business events or seasonality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The tie-breaker clause is what makes this not trivial.
Use a GROUP BY on the follows table to count followers per user, then filter for the maximum count using a subquery or window function. Ensure the query returns all users who tie for the highest follower count.
Pro tip: Mention that window functions like RANK() or DENSE_RANK() can elegantly handle ties, and discuss the trade-offs between different SQL approaches for performance and readability.
Identify the relevant columns in the follows table, typically follower_id and followee_id, where followee_id represents the user being followed.
Write a query to group by followee_id and count the number of followers for each user.
Determine the highest follower count from the grouped results, either using a subquery or a window function.
Filter the grouped results to include only users whose follower count equals the maximum, ensuring ties are handled.
If usernames are in a separate users table, join the result with that table to retrieve the usernames.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.