The join is straightforward but I tripped up on the DISTINCT count part.
Start by clarifying the schema and definitions: which table has user country, which table logs calls, and what counts as a 'video call' and 'participated'. Then write a query that filters French users, identifies those with at least one video call the previous day, and computes the percentage as the ratio of distinct participating users to total French users.
Pro tip: Always state your assumptions about the data model (e.g., user_id in calls table represents a participant, and call_date is in UTC) and mention edge cases like users with no calls or multiple calls. This shows you think like a product analyst who cares about data quality and metric definitions.
Confirm which table contains user country (e.g., user_profile) and which contains call events (e.g., calls). Define 'video call' (e.g., call_type = 'video') and 'participated' (e.g., user is caller or callee).
Select distinct user IDs from the user profile table where country = 'France' (or equivalent). This forms the denominator population.
From the calls table, filter for video calls that occurred on the previous day (using date functions relative to current_date). Extract distinct user IDs who participated.
Join or use a subquery to count how many French users are in the active set, then divide by total French users and multiply by 100. Use LEFT JOIN or conditional aggregation to handle users with no calls.
Construct the final query, ensuring correct handling of NULLs and date boundaries. Optionally, test with sample data or explain how you would validate the result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definitions of 'video call duration' and 'daily active users' (DAU) in the US for today, then outline the SQL query structure: aggregate total call duration from a calls table and count distinct active users from an activity table, both filtered by US and today's date, and finally divide the two. Emphasize handling edge cases like zero DAU and time zone considerations.
Pro tip: Mention that you would validate the metric by checking for outliers and ensuring that the date filter uses the correct time zone (e.g., US Pacific Time for Meta) to align with business reporting. Also, consider whether 'today' means the current date or the last complete day, as real-time data may be incomplete.
Define what constitutes a video call (e.g., duration > 0, completed calls) and a daily active user (e.g., any user who initiated a session). Confirm the time zone and whether 'today' refers to the current date or the last full day.
Determine the tables needed: likely a calls table with call duration and user IDs, and a user activity table with user IDs and timestamps. Ensure both have country and date fields.
Write a subquery to sum call duration for US users today, and another subquery to count distinct active users in the US today. Use appropriate date functions and filters.
Divide the total duration by the DAU count, handling division by zero (e.g., using NULLIF or CASE). Present the final metric, possibly with rounding.
Discuss potential data issues: incomplete day, time zone mismatches, bot traffic, or users with multiple calls. Suggest sanity checks like comparing to historical averages.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.