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Amazon·Data Scientist·Online Assessment (OA)·Intermediate

Intermediate
Apr 2026

Summary

Amazon Data Scientist online assessment, SQL section. Pretty standard stuff but the monthly revenue aggregation tripped me up more than I expected.

Questions Asked (1)

Q1

Given an orders table and an order_items table, write a SQL query that shows each calendar month in 2023 alongside the total revenue for that month.

Data ModelingProduct Analytics & Metrics
Author's notes

Knew I needed a join and a group by but I second-guessed myself on whether to filter by year before or after the join.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and revenue definition, then join orders and order_items on order_id, filter for 2023, and group by month using DATE_TRUNC or EXTRACT. Use a LEFT JOIN from a generated month series to ensure all 12 months appear, even those with zero revenue.

Pro tip: Explicitly state your assumptions about revenue (e.g., price * quantity) and whether to include refunds or discounts; this shows business acumen and prevents misinterpretation. Also, mention that you'd validate the query with a quick sanity check on total revenue.

1. Clarify schema and revenue definition

Ask or state assumptions about the columns in orders and order_items, and define what 'revenue' means (e.g., sum of price * quantity). Confirm whether to include all orders or only completed ones.

2. Join tables and filter for 2023

Join orders and order_items on order_id, and filter orders to the year 2023 using the order date. Ensure the join is correct (e.g., INNER JOIN if all orders have items).

3. Aggregate revenue by month

Group by the month extracted from the order date (using DATE_TRUNC('month', order_date) or EXTRACT(MONTH FROM order_date)) and sum the revenue expression.

4. Ensure all months appear

Use a calendar table or generate_series to create all 12 months of 2023, then LEFT JOIN the aggregated revenue to it, replacing NULLs with 0.

5. Format and order the output

Select the month and total revenue, order by month chronologically, and optionally format the month as 'YYYY-MM' for readability.

Key Points to Mention

  • Handling missing months with a calendar table or generate_series to avoid gaps in the report.
  • Using DATE_TRUNC or EXTRACT for month grouping, and being aware of database-specific functions.
  • Defining revenue clearly (e.g., price * quantity) and considering discounts, refunds, or taxes.
  • Filtering for the correct year (2023) using the order date, not the item date.
  • Performance considerations: indexing on order_date and order_id, and avoiding unnecessary columns in GROUP BY.
  • Validating results by cross-checking total revenue or spot-checking a month.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.