DA0-002 Data Governance Practice Question
A data analyst at a retail company is building a dashboard for store managers to track sales performance. The data comes from three sources: point-of-sale (POS) systems, inventory, and customer loyalty. The POS table contains columns transaction_id, store_id, date, product_id, quantity, and price. The inventory table has product_id, store_id, stock_level, and reorder_point. The loyalty table has customer_id, transaction_id, and points_earned. The analyst creates a star schema with a sales_fact fact table containing all rows from POS, dimension tables for store, product, date, and customer. To calculate average transaction value, the analyst uses the formula SUM(quantity * price) / COUNT(*). Store managers report that the average transaction value appears too low, especially for stores with multiple registers. The analyst realizes that because each product sold in a transaction creates a separate row in sales_fact, a single transaction with multiple items contributes multiple rows. The current calculation divides by the number of rows rather than the number of distinct transactions. Which of the following is the best course of action to correct the average transaction value metric? (Choose one.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a calculated field that sums sales per transaction (quantity * price) and then averages across distinct transaction IDs
It first sums sales per transaction (quantity * price) across all rows of the same transaction, then averages those sums across distinct transaction IDs. This directly solves the problem of dividing by the number of rows (which overcounts) because each transaction now contributes exactly one value to the average. Option A (using MEDIAN) is incorrect because median does not produce the average and does not fix the row-counting issue. Option B is too vague—simply 'aggregating' without specifying the measure and grouping could still produce incorrect results, and it does not clearly describe the correct method of summing per transaction. Option C is unnecessary; the star schema is appropriate, and the error is in the calculation, not the data model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the MEDIAN function instead of AVG
Why it's wrong here
Median does not solve the counting issue; it uses the same denominator problem.
- ✗
Aggregate the data at the transaction level before calculating the average
Why it's wrong here
This is too vague; it does not specify how to aggregate or handle the calculation.
- ✗
Use a different data model that denormalizes transaction totals into a new fact table
Why it's wrong here
Unnecessarily complex; the star schema can be used with correct calculations.
- ✓
Create a calculated field that sums sales per transaction (quantity * price) and then averages across distinct transaction IDs
Why this is correct
This correctly computes average transaction value by first summing per transaction.
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