SQL Query Prompt That Gets Working Queries

```sql
-- Purpose:
-- Retrieve a list of customers along with their total orders count and total amount spent.
-- Only include customers who have made at least one order.
-- Handle potential NULL values in order amounts by treating them as zero.
-- Exclude duplicate orders by considering distinct order IDs per customer.
-- Assumptions:
-- - There are two tables: Customers and Orders.
-- - Customers table has: CustomerID (PK), CustomerName, Email.
-- - Orders table has: OrderID (PK), CustomerID (FK), OrderAmount, OrderDate.
-- - OrderAmount can be NULL, indicating no charge or pending order (treated as 0).
-- - A customer may have zero or multiple orders.
-- - Standard ANSI SQL syntax is used.

SELECT
    c.CustomerID,
    c.CustomerName,
    c.Email,
    COUNT(DISTINCT o.OrderID) AS TotalOrders,
    COALESCE(SUM(o.OrderAmount), 0) AS TotalSpent
FROM
    Customers c
    LEFT JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY
    c.CustomerID,
    c.CustomerName,
    c.Email
HAVING
    COUNT(DISTINCT o.OrderID) > 0
ORDER BY
    TotalSpent DESC,
    c.CustomerName ASC;
```

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### Explanation

1. **Tables and Join**: 
   - We use a LEFT JOIN here to connect customers to their orders. This allows inclusion of customers even if they have no orders—but the `HAVING` clause later filters out those without any orders.
   
2. **Handling NULLs**: 
   - `COALESCE(SUM(o.OrderAmount), 0)` ensures that if all order amounts are NULL or no orders exist, the total spent is shown as zero rather than NULL.
   
3. **Counting Distinct Orders**: 
   - `COUNT(DISTINCT o.OrderID)` guards against duplicate order rows in case the join produces duplicates from additional joined tables or data anomalies.
   
4. **Filtering**:
   - The `HAVING COUNT(DISTINCT o.OrderID) > 0` clause removes customers with no orders, since the user requested to only include customers with at least one order.
   
5. **Ordering**:
   - Results are ordered by `TotalSpent` descending (highest spender first), then by `CustomerName` ascending for readability.
   
6. **Assumptions**:
   - Common customer and order attributes are used.
   - This query can serve as a base for similar reporting needs, adaptable to other schemas by renaming tables and columns.

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This query is efficient for moderate datasets, uses standard SQL functions, and carefully handles edge cases of null values and duplicates.