Start by clarifying the table schema and definitions (e.g., what constitutes a 'caller', 'recipient', and 'call'). Then outline a SQL query that filters calls from the last seven days, groups by caller and recipient to get distinct pairs, counts distinct recipients per caller, and finally counts callers with more than three distinct recipients.
Pro tip: Mention the importance of handling edge cases like multiple calls to the same recipient within the period, and consider performance implications for large datasets (e.g., using subqueries or window functions). Also, discuss how you would validate the results with a small sample.
Ask about the table structure (columns like caller_id, recipient_id, call_timestamp) and confirm definitions: 'unique callers' means distinct caller IDs, 'called' means any call (regardless of duration/status), and 'last seven days' is relative to current date.
Use a WHERE clause to select only calls where call_timestamp is within the last 7 days (e.g., call_timestamp >= CURRENT_DATE - INTERVAL '7 days').
Use SELECT DISTINCT caller_id, recipient_id to deduplicate multiple calls between the same caller and recipient within the period.
Group by caller_id and count the number of distinct recipient_id values (e.g., COUNT(DISTINCT recipient_id) or COUNT(*) on the deduplicated pairs).
Apply a HAVING clause to keep only callers with count > 3, then wrap in an outer query to count the number of such callers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the metric definition: 'customers from France' likely means users whose country is France, and 'video call' includes both 1:1 and group calls. Then, outline the data pipeline: identify the relevant tables (e.g., user dimension, call events), filter for French users and calls on the target date, and compute the percentage as (distinct users with ≥1 video call) / (total distinct French users) * 100. Finally, discuss potential data quality issues and validation steps.
Pro tip: Proactively mention that you would validate the metric by checking for bot activity, duplicate events, and time zone alignment, and consider whether to include users who made calls via cross-app experiences (e.g., Messenger video calls) if the question is about Meta overall.
Define 'customer from France' (e.g., based on user profile country or IP), 'video call' (e.g., any call with video enabled, including group calls), and 'yesterday' (calendar day in which time zone). Confirm whether the metric is for a specific app (e.g., WhatsApp) or all Meta platforms.
Locate the user dimension table for country, the call event fact table with call type and timestamp, and any session or device tables if needed. Ensure you have access to the necessary date partitions.
Use SQL or equivalent to: (a) filter users with country = 'France' and active status; (b) filter call events where call_type = 'video' and date = yesterday; (c) left join to get distinct users with at least one video call; (d) compute percentage as count(distinct users with call) / count(distinct all French users) * 100.
Check for data completeness (e.g., missing partitions), outliers (e.g., users with abnormally high call counts), and compare with historical trends. Consider segmenting by platform or user tenure to see if the percentage is consistent.
Present the percentage with confidence intervals if possible, and note any assumptions (e.g., time zone, definition of 'customer'). Suggest follow-up analyses if the number seems off.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.