← CVS Health Interview Insights
The 'minimal correct types' phrasing tripped me up a bit.
Start by clarifying the business context and access patterns, then design the schema with normalization in mind, defining each table with appropriate data types and constraints. Walk through the relationships and justify your choices, highlighting how the design supports data integrity and analytical queries.
Pro tip: Mention that while normalization reduces redundancy, you might strategically denormalize for performance in analytical workloads—showing you understand trade-offs beyond textbook design.
Ask about expected data volume, query patterns, and whether this is for transactional (OLTP) or analytical (OLAP) use. State assumptions if not provided.
Define the four tables with primary keys, foreign keys, and appropriate columns. Ensure each table represents a single entity and relationships are properly enforced.
Select minimal correct types (e.g., SERIAL for IDs, VARCHAR for names, NUMERIC for prices) and add NOT NULL, UNIQUE, and CHECK constraints to enforce data integrity.
Review the schema for normalization (3NF) and discuss potential performance implications, indexing strategies, and when denormalization might be beneficial.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero-revenue inclusion is the real ask here.
Start by clarifying the schema and definitions: identify the orders table, customer table, revenue column, and status column. Then write a query that aggregates paid orders in August 2025 per customer, left joins from customers to include those with zero revenue, and applies the specified sorting.
Pro tip: Mention that you would validate the result by checking the count of customers and sum of revenue against a quick sanity check, and discuss how you'd handle edge cases like refunds or currency conversion if applicable.
Ask about table structures, definitions of 'paid' status, revenue calculation (e.g., sum of amount), and whether customers without orders should be included.
Determine the orders table (with customer_id, order_date, status, amount) and customers table (with customer_id). Filter orders to status = 'paid' and order_date between '2025-08-01' and '2025-08-31'.
Use a subquery or CTE to sum revenue for paid orders in August 2025, grouped by customer_id.
Left join the customers table to the aggregated revenue, using COALESCE to replace NULL with 0 for customers with no paid orders.
Order the results by revenue descending, then customer_id ascending. Ensure the query is efficient and readable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you parse what 'last 7 days' means with a fixed anchor.
Start by clarifying the schema and defining the exact date window (Aug 25–Sep 1, 2025) and the status filters for non-refunded and non-cancelled orders. Then write a SQL query that joins orders to order_items and products, filters by date and status, and aggregates to get the earliest order date per product. Finally, validate the results and consider edge cases like time zones and partial refunds.
Pro tip: Explicitly state your assumptions about what 'non-refunded' and 'non-cancelled' mean (e.g., status not in ('refunded','cancelled') vs. absence of refund records) and confirm them with the interviewer—this shows attention to data semantics and prevents misinterpretation.
Ask about the table structures, status values, and whether 'non-refunded' means no refund record or a status flag. Confirm the reference date and that the window is the 7 days prior to Sep 1, 2025 (inclusive).
Set the date range as order_date >= '2025-08-25' AND order_date <= '2025-09-01'. Define the status filter to exclude orders with status 'refunded' or 'cancelled', or any order that has an associated refund.
Join orders, order_items, and products. Filter by date and status. Group by product_id and product_name, and select MIN(order_date) as earliest_order_date.
Check for time zone consistency, partial refunds, and orders with multiple items. Consider if a product appears in multiple orders—the earliest date should be the minimum across all qualifying orders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Nothing tricky here, just making sure you remember IS NULL not = NULL.
First, write an UPDATE statement that targets the orders table, filtering for paid orders in August 2025 with a NULL coupon_code, and set coupon_code to 'NONE'. Then, write a SELECT statement to count the number of rows that match the same conditions to verify the update. Ensure you use proper date filtering and consider transaction safety.
Pro tip: Always run the SELECT count before and after the UPDATE to confirm the number of affected rows, and consider wrapping the UPDATE in a transaction to allow rollback if needed. Also, be mindful of date boundaries (e.g., using >= '2025-08-01' AND < '2025-09-01') to avoid missing or including incorrect records.
Identify the target table (likely 'orders'), the condition for paid orders (e.g., status = 'paid'), the date range (August 2025), and the current state (coupon_code IS NULL).
Construct an UPDATE statement that sets coupon_code = 'NONE' for rows meeting the conditions. Use proper date filtering and ensure the WHERE clause is accurate.
Write a SELECT COUNT(*) query with the same conditions to verify the number of rows that were updated. This can be run before and after the update.
Mention wrapping the UPDATE in a transaction (BEGIN/COMMIT) to allow rollback if the count doesn't match expectations, and to avoid accidental data loss.
Walk through the logic, explain the date filtering, and discuss how you would validate the results, such as comparing counts before and after.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
You have to insert in the right order or the FK constraints blow up.
Start by identifying the foreign key dependencies between the four tables to determine the correct insertion order (parent tables first). Then, carefully map each ASCII sample value to its corresponding column, ensuring data types and formats match exactly. Finally, write INSERT statements that respect referential integrity and verify by mentally executing the inserts in order.
Pro tip: Always wrap the INSERT statements in a transaction (BEGIN/COMMIT) to ensure atomicity and to easily roll back if any constraint violation occurs. Also, explicitly list column names in the INSERT statement to avoid errors if the table schema changes.
Examine the schema to identify primary keys and foreign keys. Determine which tables are referenced by others to establish the correct insertion order.
For each table, match the ASCII sample values to the correct columns, ensuring data types (e.g., strings, dates, numbers) are correctly formatted and quoted.
Construct INSERT statements for each table, starting with parent tables. Include explicit column names and use proper syntax for values.
Check that all foreign key values in child tables exist in the parent tables. Adjust insertion order or data if necessary to avoid constraint violations.
Mentally execute the inserts in order, or if possible, run them in a test environment to ensure they reproduce the data exactly without errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.