Why project structure scales with team size in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a dbt model that selects all columns from the source table.

dbt

select * from [1]

Drag options to blanks, or click blank then click option'

Atable('raw_data')

Bsource('raw', 'table')

Cref('source_table')

Ddataset('source')

Attempts:

3 left

💡 Hint

Common Mistakes

Using ref() instead of source() for raw tables.

Using incorrect function names like table() or dataset().

In dbt, source() is used to refer to raw data tables defined in the sources.yml file.

2fill in blank

medium

Complete the code to create a model that references another model named 'customers'.

dbt

select * from [1]

Drag options to blanks, or click blank then click option'

Aref('customers')

Bsource('raw', 'customers')

Ctable('customers')

Ddataset('customers')

Attempts:

3 left

💡 Hint

Common Mistakes

Using source() instead of ref() for models.

Using non-dbt functions like table().

Use ref() to reference other dbt models within your project.

3fill in blank

hard

Fix the error in the dbt model code to correctly filter customers with more than 5 orders.

dbt

select customer_id, count(order_id) as order_count
from [1]
group by customer_id
having order_count > 5

Drag options to blanks, or click blank then click option'

Adataset('orders')

Bsource('raw', 'orders')

Ctable('orders')

Dref('orders')

Attempts:

3 left

💡 Hint

Common Mistakes

Using source() for models instead of ref().

Using non-dbt functions like table().

Use ref() to reference the 'orders' model, which is needed for aggregation.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps model names to their descriptions if the description exists.

dbt

model_descriptions = {model: [1] for model, [2] in models.items() if description}

Drag options to blanks, or click blank then click option'

Adescription

Bdesc

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for both key and value.

Using undefined variable names.

The comprehension uses description as the value and desc as the variable name for descriptions.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of models with more than 100 rows.

dbt

large_models = { [1]: [2] for [3], [2] in models.items() if [2]['row_count'] > 100 }

Drag options to blanks, or click blank then click option'

Amodel_name

Bmodel_info

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up key and value variable names.

Using inconsistent variable names in the loop.

The dictionary comprehension uses model_name as the key and model_info as the value, iterating over model_name, model_info.