Why testing ensures data quality in dbt - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a basic test that checks if a column has no null values.

dbt

version: 2
models:
  - name: customers
    columns:
      - name: email
        tests:
          - [1]

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

Anot_null

Brelationships

Caccepted_values

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'unique' which checks for duplicates, not nulls.

Using 'accepted_values' which checks for allowed values, not nulls.

The not_null test ensures that the column has no missing values, which is a basic data quality check.

2fill in blank

medium

Complete the code to add a test that checks if values in the 'status' column are within allowed values.

dbt

version: 2
models:
  - name: orders
    columns:
      - name: status
        tests:
          - accepted_values:
              values: [1]

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

A['yes', 'no']

B['open', 'closed']

C['active', 'inactive']

D['pending', 'shipped', 'delivered']

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing values unrelated to order status.

Using an empty list or wrong format.

The accepted_values test checks that the 'status' column only contains the specified list of allowed values.

3fill in blank

hard

Fix the error in the test definition that checks for unique values in the 'user_id' column.

dbt

version: 2
models:
  - name: sessions
    columns:
      - name: user_id
        tests:
          - [1]

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

Anot_null

Bunique

Caccepted_values

Drelationships

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'not_null' which only checks for missing values.

Using 'relationships' which checks foreign key constraints.

The unique test ensures that each 'user_id' appears only once, which is important for identifying distinct users.

4fill in blank

hard

Fill both blanks to create a test that checks if 'order_id' in 'orders' model exists in 'order_details' model.

dbt

version: 2
models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - [1]:
              to: [2]

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

Arelationships

Borders.order_id

Cref('order_details.order_id')

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'unique' instead of 'relationships'.

Referencing the wrong table or column in 'to'.

The relationships test checks foreign key constraints. Here, it verifies that 'order_id' in 'orders' exists in 'order_details'.

5fill in blank

hard

Fill all three blanks to define a custom test that checks if the 'age' column values are greater than 18.

dbt

version: 2
models:
  - name: users
    tests:
      - name: age_check
        test: "select * from [1] where [2] [3] 18"

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

Aref('users')

Bage

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong table name.

Using '>' instead of '<=' for the condition.

Referencing the wrong column.

This custom test selects rows where age <= 18 from the users model. The test passes only if zero rows are returned, ensuring all ages are greater than 18.