dbt-utils package tests - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dbt_utils.equality test on two tables

Given two tables table_a and table_b with identical columns and data, what will be the result of running the dbt_utils.equality test comparing these tables?

dbt

select * from {{ dbt_utils.equality('table_a', 'table_b') }}

A Test passes with no failures

B Test fails showing all rows as mismatched

C Syntax error due to missing parameters

D Test returns an empty result set but marked as failed

💡 Hint

Think about what happens when two tables have exactly the same data.

The dbt_utils.equality test compares two tables row by row. If they are identical, the test passes with no failures.

❓ data_output

intermediate

Result of dbt_utils.unique_combination_of_columns test

You run the dbt_utils.unique_combination_of_columns test on a table with columns user_id and order_id. The table has duplicate rows where user_id and order_id repeat. What will the test output show?

dbt

select * from {{ dbt_utils.unique_combination_of_columns('orders', ['user_id', 'order_id']) }}

A Only unique rows with no duplicates

B Rows that have duplicate combinations of user_id and order_id

C All rows in the table

D An error because the columns list is not a string

💡 Hint

The test finds duplicates in the specified columns.

The test returns rows where the combination of specified columns is not unique, showing duplicates.

🔧 Debug

advanced

Identify the error in dbt_utils.not_null test usage

What error will occur when running this test?

select * from {{ dbt_utils.not_null('customers', 'customer_id, email') }}

dbt

select * from {{ dbt_utils.not_null('customers', 'customer_id, email') }}

A Test returns rows with nulls in customer_id only

B Test passes with no errors

C Runtime error because table does not exist

D Compilation error due to passing columns as a single string instead of a list

💡 Hint

Check the expected data type for the columns parameter.

The columns parameter must be a list of strings, not a single string with commas.

🚀 Application

advanced

Using dbt_utils.expression_is_true to test a condition

You want to test that all rows in the sales table have amount > 0. Which is the correct way to write this test using dbt_utils.expression_is_true?

A select * from {{ dbt_utils.expression_is_true('sales', amount > 0) }}

B select * from {{ dbt_utils.expression_is_true('sales', 'amount >= 0') }}

C select * from {{ dbt_utils.expression_is_true('sales', 'amount > 0') }}

D select * from {{ dbt_utils.expression_is_true('sales', ['amount > 0']) }}

💡 Hint

The expression parameter must be a string representing the condition.

The expression must be passed as a string. Option C correctly passes 'amount > 0' as a string.

🧠 Conceptual

expert

Understanding the output of dbt_utils.surrogate_key test

You use dbt_utils.surrogate_key macro to generate a surrogate key from columns first_name and last_name. What is the expected output type and behavior of this macro?

A It returns a hashed string combining the specified columns, suitable as a unique key

B It returns a numeric auto-incrementing ID for each row

C It returns a concatenated string of the columns separated by underscores

D It returns a boolean indicating if the columns are unique

💡 Hint

Think about what a surrogate key is and how hashing helps.

The surrogate_key macro creates a hash of the specified columns to produce a unique string key.

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