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Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jun 2024

Summary

Amazon data scientist interview with a SQL-heavy technical screen. Two questions, both centered on aggregate functions and window functions across retail-style schemas. Nothing too wild but you need to be comfortable writing real SQL under pressure.

Questions Asked (2)

Q1

Given a SALES table with columns for product_id, revenue, and quantity (which can be NULL), write SQL to compute average and standard deviation of revenue grouped by product, and add a column that replaces NULL quantity values with 0.

Product Analytics & MetricsData Modeling
Author's notes

Pretty standard aggregation question but the NULL handling tripped me up slightly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and business context, then write a SQL query that groups by product_id, computes AVG(revenue) and STDDEV(revenue), and uses COALESCE(quantity, 0) to handle NULLs. Explain each function and consider edge cases like NULL revenues or empty groups.

Pro tip: Mention that STDDEV in SQL often defaults to sample standard deviation, but you can use STDDEV_POP for population—clarify which is appropriate for the business question. Also, note that COALESCE is more portable than ISNULL or IFNULL.

1. Clarify requirements and schema

Confirm the table structure, data types, and whether revenue can be NULL. Ask if the average and standard deviation should be computed on revenue or another metric, and whether NULL quantities should be replaced before or after aggregation.

2. Handle NULL quantities

Use COALESCE(quantity, 0) to replace NULLs with 0. This can be done in a subquery or directly in the SELECT statement, but ensure it doesn't affect the aggregation of revenue.

3. Write aggregation query

Group by product_id and compute AVG(revenue) and STDDEV(revenue) (or STDDEV_POP). Include the cleaned quantity column if needed, but note that it may not be aggregated.

4. Validate and optimize

Check for edge cases like products with no sales or NULL revenues. Consider indexing product_id for performance and test the query on sample data.

Key Points to Mention

  • Use of COALESCE to handle NULLs in quantity
  • Difference between STDDEV and STDDEV_POP (sample vs population)
  • Grouping by product_id and aggregating revenue
  • Handling NULL revenues in AVG and STDDEV (they are ignored by default)
  • Portability of functions across SQL dialects (e.g., COALESCE vs IFNULL)
  • Potential need for a subquery to clean data before aggregation

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

Q2

Using the ORDERS, ORDER_ITEMS, and CUSTOMERS tables, write SQL to join them and compute total revenue per customer, then rank customers by total revenue within each region using both RANK and DENSE_RANK.

Product Analytics & MetricsAlgorithms & Data Structures
Author's notes

This one was more interesting.

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AI HintsAI Generated

Suggested Approach

Start by joining the three tables to get order-level revenue, then aggregate to customer-level total revenue. Use window functions RANK and DENSE_RANK partitioned by region and ordered by total revenue descending, and explain the difference between them.

Pro tip: Mention that RANK leaves gaps for ties while DENSE_RANK does not, and that the choice depends on whether you want to preserve the ranking order or avoid gaps. Also, clarify that revenue should be computed as SUM(quantity * unit_price) and consider handling NULLs or returns.

1. Understand the schema and define revenue

Identify the join keys: CUSTOMERS.customer_id = ORDERS.customer_id and ORDERS.order_id = ORDER_ITEMS.order_id. Define revenue as quantity * unit_price (or price) from ORDER_ITEMS.

2. Join tables and compute order-level revenue

Write a query that joins CUSTOMERS, ORDERS, and ORDER_ITEMS, and calculates revenue per order line. Use INNER JOINs assuming all orders have items and customers.

3. Aggregate total revenue per customer

Group by customer_id (and region) and sum the revenue to get total revenue per customer. Include customer name and region for readability.

4. Apply window functions for ranking

Use RANK() and DENSE_RANK() with PARTITION BY region ORDER BY total_revenue DESC. Explain that RANK skips numbers after ties, while DENSE_RANK does not.

5. Present final query and discuss edge cases

Show the complete SQL, and mention handling ties, NULLs, and potential performance considerations (e.g., indexing on join keys).

Key Points to Mention

  • Correct join conditions between CUSTOMERS, ORDERS, and ORDER_ITEMS.
  • Revenue calculation: SUM(quantity * unit_price) or equivalent.
  • Aggregation: GROUP BY customer_id and region to get total revenue per customer.
  • Window functions: RANK() and DENSE_RANK() with PARTITION BY region ORDER BY total_revenue DESC.
  • Difference between RANK and DENSE_RANK: RANK leaves gaps for ties, DENSE_RANK does not.
  • Potential edge cases: NULL values, customers with no orders, returns/refunds, and performance considerations.

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