dbt-utils package tests - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the dbt-utils package in dbt?

The dbt-utils package provides a collection of useful macros and tests that simplify common data transformation and testing tasks in dbt projects.

beginner

Name one common test provided by dbt-utils and explain its use.

The unique_combination_of_columns test checks that the combination of specified columns in a model is unique, ensuring no duplicate rows based on those columns.

intermediate

How do you add dbt-utils tests to your dbt project?

You add dbt-utils as a package in your packages.yml file, run dbt deps to install it, then reference its tests in your model's schema.yml file.

beginner

What does the dbt_utils.not_null_proportion test check for?

It checks that the specified column(s) in a model have at least a minimum proportion of non-NULL values (e.g., 1.0 for no NULLs), ensuring data completeness.

intermediate

Explain how the dbt_utils.expression_is_true test works.

This test runs a SQL expression you provide and checks that it evaluates to true for all rows in the model, allowing custom validation logic.

Which file do you update to add dbt-utils as a dependency?

A schema.yml

B dbt_project.yml

C profiles.yml

D packages.yml

The packages.yml file is used to declare external packages like dbt-utils.

What does the unique_combination_of_columns test ensure?

A The combination of specified columns is unique

B Columns have unique values individually

C No NULL values in columns

D Columns contain only numeric data

It ensures that the combination of the specified columns does not have duplicate rows.

Which command installs the dbt-utils package after adding it to packages.yml?

A dbt deps

B dbt run

C dbt test

D dbt compile

dbt deps downloads and installs packages listed in packages.yml.

What does the not_null_proportion test check in dbt-utils?

A That columns have unique values

B That columns have default values

C That columns do not contain NULL values

D That columns contain only numbers

The not_null_proportion test ensures no NULL values exist in the specified columns when the threshold is set to 1.0.

How do you specify a dbt-utils test in your model's schema.yml?

A By writing SQL queries in the model file

B By adding a <code>tests</code> section with the test name and arguments

C By modifying <code>dbt_project.yml</code>

D By running <code>dbt test</code> without configuration

You add tests in schema.yml under the model's columns or at the model level with test names and parameters.

Describe the steps to add and use a dbt-utils test in your dbt project.

Think about package installation and test configuration.

Explain the difference between the not_null_proportion and unique_combination_of_columns tests in dbt-utils.

Focus on what each test validates about the data.

Practice

1. What is the main purpose of using dbt-utils package tests in your data models?

easy

A. To write complex SQL queries faster

B. To speed up data loading processes

C. To check data quality by validating conditions like uniqueness and nulls

D. To create visual dashboards automatically

Solution

1. Step 1: Understand the role of dbt-utils tests

   dbt-utils tests are designed to check data quality by validating conditions such as uniqueness, null values, and accepted values.

2. Step 2: Compare options with the purpose

   Only To check data quality by validating conditions like uniqueness and nulls describes checking data quality, which matches the purpose of dbt-utils tests.

3. Final Answer:

   To check data quality by validating conditions like uniqueness and nulls -> Option C

4. Quick Check:

   dbt-utils tests = data quality checks [OK]

Hint: Tests in dbt-utils check data quality, not speed or visuals [OK]

Common Mistakes:

• Thinking dbt-utils speeds up data loading
• Confusing tests with dashboard creation
• Assuming tests write SQL queries automatically

2. Which of the following is the correct syntax to add a uniqueness test using dbt-utils in a model YAML file?

easy

A. tests:
- dbt_utils.unique:
column_name: column_name

B. tests: - dbt_utils.unique: ['column_name']

C. tests: - dbt_utils.unique - column_name

D. tests: - dbt_utils.unique: {column_name}

Solution

1. Step 1: Recall dbt-utils test syntax in YAML

   The correct syntax uses column_name parameter under the test, like dbt_utils.unique: column_name: column_name.

2. Step 2: Check each option format

   tests: - dbt_utils.unique: column_name: column_name correctly uses the column_name key-value pair, matching dbt-utils documentation.

3. Final Answer:

   tests: - dbt_utils.unique: column_name: column_name -> Option A

4. Quick Check:

   Unique test syntax = column_name key [OK]

Hint: Use column_name: column_name for dbt_utils.unique [OK]

Common Mistakes:

• Broken YAML with multiple dashes
• Using list ['column_name'] instead of column_name key
• Using curly braces for parameters

3. Given this YAML snippet in a dbt model file:

tests:
  - dbt_utils.not_null:
      column_name: user_id
  - dbt_utils.unique:
      column_name: user_id

What will happen when you run dbt test?

medium

A. dbt will throw a syntax error due to wrong YAML format

B. dbt will check that user_id has no nulls and is unique

C. dbt will only check for nulls in user_id

D. dbt will ignore these tests and run default tests

Solution

1. Step 1: Analyze YAML test syntax

   The syntax uses column_name: user_id under dbt_utils tests, which is the correct format for not_null and unique tests.

2. Step 2: Identify dbt behavior

   This correct format will cause dbt to run both tests: no nulls and uniqueness on user_id.

3. Final Answer:

   dbt will check that user_id has no nulls and is unique -> Option B

4. Quick Check:

   Correct YAML format = runs both tests [OK]

Hint: dbt-utils tests use column_name: col correctly [OK]

Common Mistakes:

• Using lists [- user_id] instead of column_name key
• Wrong YAML indentation
• Missing quotes around column_name value

4. You wrote this test in your model YAML:

tests:
  - dbt_utils.accepted_values:
      column_name: ['status']
      values: ['active', 'inactive']

But when running dbt test, it fails with an error. What is the likely problem?

medium

A. The column_name should be a string, not a list

B. The values parameter should be a list under accepted_values, not a key-value pair

C. The accepted_values test requires a where clause

D. The values list must be a string, not a list

Solution

1. Step 1: Review dbt-utils accepted_values test syntax

   The accepted_values test expects column_name as a string identifier, not a list.

2. Step 2: Identify the YAML structure issue

   column_name: ['status'] passes a list instead of string 'status', causing an error.

3. Final Answer:

   The column_name should be a string, not a list -> Option A

4. Quick Check:

   accepted_values column_name = string [OK]

Hint: column_name must be string for accepted_values test [OK]

Common Mistakes:

• Passing column_name as list instead of string
• Using string instead of list for values
• Adding unnecessary where clause

5. You want to ensure a column email in your users model has no nulls and all values are unique using dbt-utils tests. Which YAML snippet correctly applies both tests?

hard

A. tests:
- dbt_utils.not_null:
email
- dbt_utils.unique:
email

B. tests:
- dbt_utils.not_null:
columns: ['email']
- dbt_utils.unique:
columns: ['email']

C. tests:
- dbt_utils.not_null:
- email
- dbt_utils.unique:
- email

D. tests:
- dbt_utils.not_null:
column_name: email
- dbt_utils.unique:
column_name: email

Solution

1. Step 1: Recall correct dbt-utils test syntax for columns

   dbt-utils not_null and unique tests expect column_name: email under the test name.

2. Step 2: Evaluate each YAML snippet

   Snippet with column_name: email for both tests correctly uses the required key-value format. The snippet missing colons after tests is invalid YAML. Using a list - email passes incorrect config type. Using columns is invalid key.

3. Final Answer:

   tests: - dbt_utils.not_null: column_name: email - dbt_utils.unique: column_name: email -> Option D

4. Quick Check:

   column_name: [col] under test name [OK]

Hint: Use column_name: email for not_null and unique tests [OK]

Common Mistakes:

• Omitting colon and using scalar email
• Using list - email for single-column tests
• Using wrong key columns instead of column_name