Start by clarifying the schema: identify the revenue table, the date/timestamp column, the revenue amount column, and the creation source column. Then write a SQL query that groups by date and creation source, summing revenue, and consider whether to include all dates or only dates with revenue. Finally, discuss how you would validate the results and handle edge cases like nulls or time zones.
Pro tip: Mention that you would check for duplicate transactions or refunds that might skew daily revenue, and consider whether to use a calendar table to ensure all dates are represented even if no revenue occurred.
Ask about the table structure, column names, and what 'creation source' means (e.g., organic, paid, referral). Confirm the revenue metric (e.g., gross revenue, net revenue) and the date granularity (daily).
Determine that you need to group by date and creation source, and sum the revenue column. Consider whether to truncate timestamps to date and how to handle time zones.
Construct a SELECT statement with DATE(created_at) AS date, creation_source, and SUM(revenue) AS total_revenue, grouping by date and creation_source, and ordering by date and creation_source.
Discuss handling NULL creation sources, excluding test accounts, and ensuring data completeness. Mention how you would validate the query output against known totals or a sample.
If needed, mention indexing on date and creation_source for performance. Present the query clearly and explain the logic behind each part.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up slightly because advertiser 19 (Gamma GmbH) has no active ads at all and only exists in the advertiser_info table.
Start by clarifying the schema: identify the tables for advertisers, ads, and countries, and define what 'active' means (e.g., status = 'active'). Then write a query that counts active ads per advertiser, orders by count ascending, limits to 10, and joins to the country table to return the country names.
Pro tip: Mention that you would confirm the definition of 'active' with stakeholders, as it might include date ranges or other statuses, and note that ties can be broken arbitrarily but you'd use a deterministic tiebreaker like advertiser_id for reproducibility.
Identify the relevant tables (e.g., advertisers, ads, countries) and the columns that link them. Confirm what 'active' means (e.g., status = 'active', or end_date > current_date).
Write a subquery or CTE that filters ads to only active ones, groups by advertiser_id, and counts the number of ads. Ensure advertisers with zero active ads are included if they exist in the advertiser table.
Order the results by the active ad count ascending, and limit to 10 rows. If ties matter, decide on a tiebreaker (e.g., advertiser_id) and include it in the ORDER BY.
Join the limited result set to the advertisers table (if not already joined) and then to the countries table to retrieve the country names. Select the country column as the final output.
Check for NULLs, advertisers without a country, or duplicate country entries. Discuss how you would handle them (e.g., use LEFT JOIN, COALESCE, or DISTINCT).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definitions of 'creation source' and 'advertiser' (e.g., account level, campaign level) and the time period (year-over-year). Then, for each creation source, compute the proportion of advertisers whose total spend this year exceeds last year's total spend by at least $1,000, ensuring proper handling of missing data and outliers.
Pro tip: Consider whether to include advertisers with zero spend in either year, as this can significantly affect proportions; also, segment by advertiser size or tenure to uncover actionable insights.
Confirm what 'creation source' means (e.g., how the advertiser was acquired) and the unit of analysis (advertiser account). Define the time periods (e.g., calendar year vs. rolling 12 months) and currency (USD).
Aggregate spend per advertiser per year, ensuring consistent advertiser IDs across years. Handle missing data, outliers, and currency conversions if needed.
For each advertiser, calculate the difference in spend (this year minus last year). Flag advertisers with a difference of at least $1,000.
For each creation source, compute the proportion of advertisers meeting the $1,000 threshold. Use the total number of advertisers in that source as the denominator.
Check for statistical significance if comparing sources, and consider segmenting by advertiser size or other dimensions to provide deeper insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.