You have a sales table with columns: region, product, jan, feb, mar. You want to transform monthly sales columns into rows with columns month and sales. Which dbt-utils macro and usage is correct?

hard📝 Application Q8 of 15

dbt - Packages and Reusability

You have a sales table with columns: region, product, jan, feb, mar. You want to transform monthly sales columns into rows with columns month and sales. Which dbt-utils macro and usage is correct?

A{{ dbt_utils.unpivot(relation=ref('sales'), columns=['jan', 'feb', 'mar'], name_column='month', value_column='sales') }}

B{{ dbt_utils.pivot(relation=ref('sales'), column='month', value='sales') }}

C{{ dbt_utils.surrogate_key(['region', 'product']) }}

D{{ dbt_utils.aggregate(['jan', 'feb', 'mar']) }}

Step-by-Step Solution

Solution:

Step 1: Identify transformation goal
Converting columns jan, feb, mar into rows is unpivoting.
Step 2: Match macro usage
unpivot macro with columns list and name/value columns matches the goal.
Final Answer:
{{ dbt_utils.unpivot(relation=ref('sales'), columns=['jan', 'feb', 'mar'], name_column='month', value_column='sales') }} -> Option A
Quick Check:
unpivot converts columns to rows [OK]

Quick Trick: Use unpivot to turn multiple columns into rows [OK]

Common Mistakes:

MISTAKES

Using pivot instead of unpivot
Confusing surrogate_key with reshaping macros
Assuming aggregate macro exists

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You have a sales table with columns: region, product, jan, feb, mar. You want to transform monthly sales columns into rows with columns month and sales. Which dbt-utils macro and usage is correct?

Step 1: Identify transformation goal

Step 2: Match macro usage

Final Answer:

Quick Check:

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