## 📸 File History Snapshot (2025-12-05T16:10:32.303Z)
## ◂ USER
  resume
## ◆ ASSISTANT
  It looks like you'd like to continue from where we left off, but I don't have context from a previous conversation in this session.
  
  Could you let me know what you were working on? For example:
  - A SQL query or database task
  - Something with your website
  - A file or report you were creating
  - Another project
  
  I'm ready to pick up wherever you need!
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## 📸 File History Snapshot (2025-12-05T16:11:26.304Z)
## ◂ USER
  sql works that we left off
## ◆ ASSISTANT
  I don't have access to our previous conversation history, so I'm not sure which SQL task we were working on.
  
  Could you remind me what we were doing? For example:
  
  - A specific query you needed help with
  - A report or analysis
  - Creating/modifying tables
  - Something with the weekly sales, inventory, or delivery data
  
  Just give me a quick summary and I'll jump right back in!
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## 📸 File History Snapshot (2025-12-05T16:13:21.014Z)
## ◂ USER
  this task: SQL Query Interview Examples: 
  EXAMPLE 1: 
  Given two tables: 
  Table Name - Columns 
  Transactions - transaction_id, customer_id, date, product_id, quantity 
  Product - product_id, product_category, unit_price 
## ◆ ASSISTANT
  Got it! Let's continue with the SQL interview practice.
  
  From the example you shared:
  
  **Tables:**
  - `Transactions` - transaction_id, customer_id, date, product_id, quantity
  - `Product` - product_id, product_category, unit_price
  
  These map nicely to your practice database:
  - `order_items` → similar to Transactions (has order_id, product_id, quantity, unit_price)
  - `orders` → adds customer_id, order_date
  - `products` → similar to Product (has product_id, category, unit_cost)
  
  **What's the interview question for Example 1?** Common ones with this setup include:
  
  1. Find total revenue by product category
  2. Find top N customers by spending
  3. Calculate running totals or month-over-month growth
  4. Find products that have never been purchased
  5. Identify customers who bought from multiple categories
  
  Which question were we working on, or would you like to try one of these?
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## 📸 File History Snapshot (2025-12-05T16:15:38.589Z)
## ◂ USER
  Output the list of customers and the % of their total spend in each product category. what do they mean the % of their total spend in each product category
## ◆ ASSISTANT
  Good question! Let me break down what they're asking for:
  
  **"% of their total spend in each product category"** means:
  
  For each customer, calculate what percentage of their money went to each category.
  
  **Example:**
  
  Say Customer A spent:
  - $300 on Electronics
  - $100 on Clothing
  - $100 on Food
  - **$500 total**
  
  The output would be:
  
  | customer_id | category    | spend_pct |
  |-------------|-------------|-----------|
  | A           | Electronics | 60%       |
  | A           | Clothing    | 20%       |
  | A           | Food        | 20%       |
  
  **The formula:**
  
  ```
  % in category = (spend in that category / total spend across all categories) × 100
  ```
  
  So it shows how each customer distributes their spending. This is useful for understanding customer preferences - do they buy mostly one category, or spread across many?
  
  Would you like to work through writing this query together?
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