Start by clarifying the schema and definitions (e.g., what constitutes a 'Live session', how to identify mobile sessions, and how to define 'first week of October 2022' and 'US'). Then, write a SQL query that filters sessions by date range, country, and device type, groups by creator, counts distinct sessions, and finally filters creators with more than 3 sessions. Validate assumptions and consider edge cases like time zones and session boundaries.
Pro tip: Mention that you would confirm whether 'first week' means Oct 1-7 or the first full week (e.g., Oct 3-9) and whether 'mobile' includes tablets. Also, discuss how to handle sessions that span midnight or cross week boundaries.
Ask clarifying questions to define key terms: 'Live session', 'mobile', 'first week of October 2022', 'US', and 'distinct creators'. Confirm the date range and whether to include sessions that start before but end within the week.
Identify relevant columns in the Live sessions and activity tables, such as creator_id, session_id, start_time, end_time, device_type, country, and any join keys. Determine how to link tables if needed.
Write a subquery to filter sessions by date range (first week of Oct 2022), country = 'US', and device_type = 'mobile'. Group by creator_id and count distinct session_id to get the number of sessions per creator.
From the aggregated result, filter creators with session count > 3, then count the distinct creators to get the final answer.
Sanity-check results (e.g., total sessions, distribution) and discuss potential pitfalls like time zone conversions, session overlap, or missing data. Suggest alternative interpretations if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, filter the gift transactions to October 2022 and aggregate total gifts received per creator per country. Then, within each country, rank creators by total gifts descending and select the top 5, using a deterministic tie-breaker such as creator ID ascending. Finally, output the results grouped by country.
Pro tip: Explicitly state your tie-breaking rule (e.g., 'ties broken by creator ID ascending') and mention that you would validate the results by checking for duplicate creators per country and ensuring exactly 5 rows per country where possible.
Filter the transaction data to October 2022 and compute the sum of gifts received for each creator within each country.
For each country, rank creators by total gifts in descending order. Apply a deterministic tie-breaker, such as creator ID ascending, to ensure consistent ordering.
From the ranked list, pick the top 5 creators for each country. If there are fewer than 5 creators, include all.
Check that each country has at most 5 creators and no duplicates. Format the final result with country, creator ID, and total gifts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'every room must satisfy the condition' part is what makes this hard.
Clarify the definitions of 'creator', 'went live', 'room', and 'watched by 500 or more viewers' to ensure alignment. Then outline a SQL-based approach that aggregates live sessions per creator per day, filters for creators with at least two sessions, and checks that the minimum viewer count across all their rooms is >= 500. Finally, count the distinct creators meeting both conditions.
Pro tip: Mention the importance of handling edge cases like null viewer counts, multiple rooms per session, and timezone considerations for 'yesterday's date'. Also, discuss how to optimize the query for performance on large datasets, such as using window functions or subqueries.
Ask clarifying questions to define key terms: what constitutes a 'creator', a 'live session', a 'room', and how 'watched by 500 or more viewers' is measured (e.g., peak concurrent viewers, unique viewers, average viewers). Confirm the date range for 'yesterday' and timezone.
Determine which tables contain live session data, creator information, and viewer metrics. Typically, there would be a sessions table with creator_id, session_id, start_time, and a viewers table or a column with viewer counts per session.
For the given date, group by creator_id and count the number of live sessions (or rooms). Filter to only creators with count >= 2. Simultaneously, compute the minimum viewer count across all their sessions for that day.
From the aggregated results, filter to creators whose minimum viewer count is >= 500. This ensures every room had at least 500 viewers.
Count the distinct creators that satisfy both conditions. Validate the result by checking edge cases, such as creators with exactly two sessions, and ensure no double-counting due to multiple rooms per session.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.