Straightforward join and count distinct, nothing fancy.
Start by clarifying the schema and the exact definition of 'distinct article types viewed'—likely counting unique article types per user per day. Then, write a SQL query that joins the views table with the articles table on article_id, filters for the specific date, groups by user_id, and counts distinct article types.
Pro tip: Mention that you would first check for data quality issues like duplicate views or missing article types, and consider using a subquery or CTE to filter the date before joining to improve performance.
Ask about the table structures, column names, and whether 'specific date' is a parameter or hardcoded. Confirm that 'distinct article types' means unique type values per user.
Use a WHERE clause to restrict the views table to the specific date, reducing the dataset before joining.
Join the filtered views with the articles table on article_id to associate each view with its article type.
Group the joined result by user_id and use COUNT(DISTINCT article_type) to get the number of distinct types viewed per user.
Consider users with no views on that date (may need LEFT JOIN or separate handling) and validate results with a small sample.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify that the previous query returns a per-user count of distinct article types viewed on the given date. Then, wrap that query in a subquery or CTE and apply a GROUP BY on the distinct type count to get the histogram of users by number of article types viewed.
Pro tip: Mention that you would validate the histogram by checking that the sum of user counts equals the total number of active users on that date, and consider whether zero-type users should be included based on the business question.
Ensure the previous query correctly computes, for each user, the number of distinct article types viewed on the specified date. Confirm the date filter and that the query returns one row per user with a count column.
Use the previous query as a subquery or CTE so you can aggregate over its results. This keeps the logic modular and avoids repeating complex joins or filters.
In the outer query, group by the count of distinct article types and count the number of users in each group. This produces the histogram: number of users who viewed exactly 1 type, exactly 2 types, etc.
Order the results by the number of article types (ascending) to make the histogram easy to read. Optionally, include a row for users with 0 types if relevant, and ensure the output columns are clearly labeled.
Check that the sum of users across all buckets equals the total number of users who viewed at least one article type on that date. Discuss any insights, such as the most common number of article types viewed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.