In dbt, models are the main building blocks. Which statement best explains why models are the core of dbt?
Think about what dbt does with data and how models fit in.
Models in dbt are SQL files that define how raw data is transformed into clean, organized tables or views. They are the core because they represent the transformation logic.
Given this dbt model SQL code, what will be the output table content?
select id, upper(name) as name_upper from raw.customers where active = true
Look at the filter and the upper function.
The query selects only active customers and converts their names to uppercase. So only active customers appear with uppercase names.
If you have 5 SQL files in your models folder and run dbt run, how many models will be created in your data warehouse?
Each SQL file in the models folder corresponds to one model.
Each SQL file in the models folder is compiled and run as one model, so 5 files produce 5 models.
What error will this dbt model SQL produce when run?
select id, name from raw.customers where active = 'yes'
Check the data type of the 'active' column and the filter value.
If 'active' is a boolean column, comparing it to string 'yes' causes a runtime error due to type mismatch.
You want your dbt model to update quickly and reflect the latest data without rebuilding the entire table. Which materialization should you choose?
Think about materializations that update only new or changed data.
Incremental materialization updates only new or changed rows, making it faster for large datasets than rebuilding tables or views.