Data mesh patterns with dbt - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this dbt model run?

Given the following dbt model SQL, what will be the resulting table's row count if the source table has 1000 rows with 200 rows having status = 'active'?

dbt

select * from {{ ref('source_table') }} where status = 'active'

A 1000 rows

B 200 rows

C 800 rows

D 0 rows

💡 Hint

Think about the filter condition in the SQL query.

The model filters rows where status is 'active'. Since 200 rows match, the output table will have 200 rows.

❓ data_output

intermediate

What is the resulting column in this dbt model?

This dbt model adds a new column 'is_high_value' based on amount. What will be the values for amount 50 and 150?

dbt

select
  id,
  amount,
  case when amount > 100 then true else false end as is_high_value
from {{ ref('orders') }}

A For 50: false, For 150: true

B For 50: true, For 150: false

C For 50: true, For 150: true

D For 50: false, For 150: false

💡 Hint

Check the condition amount > 100 carefully.

Amounts greater than 100 get true, others false. So 50 is false, 150 is true.

🔧 Debug

advanced

Why does this dbt model fail to compile?

This dbt model SQL throws a compilation error. Identify the cause.

dbt

select id, amount
from {{ ref('orders') }}
where amount > 100

A Missing closing brace in ref function

B Missing comma between columns

C Incorrect table name in ref

D Invalid where clause syntax

💡 Hint

Check the syntax of the Jinja ref function.

The ref function is missing a closing brace '}}', causing compilation failure.

🚀 Application

advanced

Which dbt pattern supports decentralized data ownership in data mesh?

In data mesh, teams own their data domains. Which dbt pattern best supports this principle?

A Centralizing all models in one dbt project

B Using a single schema for all data

C Using separate dbt projects per domain

D Avoiding version control for models

💡 Hint

Think about how to isolate domain ownership in dbt.

Separate dbt projects per domain allow teams to own and manage their data independently, aligning with data mesh principles.

🧠 Conceptual

expert

What is the main benefit of using dbt's source freshness feature in a data mesh?

Why is source freshness important when implementing data mesh patterns with dbt?

A It disables incremental model builds

B It automatically fixes data quality issues in source tables

C It merges all domain data into a single table

D It ensures data domains publish timely and reliable data for consumers

💡 Hint

Consider how freshness impacts trust in shared data.

Source freshness checks help teams monitor data recency, ensuring domains provide up-to-date data, which is critical for trust in data mesh.

Practice

1. What is the main purpose of using data mesh patterns with dbt?

easy

A. To write complex Python scripts for data processing

B. To enable teams to share clean and trusted data easily

C. To create visual dashboards directly in dbt

D. To store raw data without transformation

Solution

1. Step 1: Understand data mesh goals

   Data mesh patterns focus on decentralizing data ownership and sharing clean, trusted data across teams.

2. Step 2: Role of dbt in data mesh

   dbt helps build and test data models, making data reliable and easy to share.

3. Final Answer:

   To enable teams to share clean and trusted data easily -> Option B

4. Quick Check:

   Data mesh = share clean data [OK]

Hint: Data mesh is about sharing clean data across teams [OK]

Common Mistakes:

• Confusing dbt with dashboard tools
• Thinking data mesh stores raw data only
• Assuming dbt runs Python scripts

2. Which of the following is the correct way to reference another model in a dbt SQL file?

easy

A. SELECT * FROM ref('model_name')

B. SELECT * FROM model_name()

C. SELECT * FROM {{ ref('model_name') }}

D. SELECT * FROM $ref.model_name

Solution

1. Step 1: Recall dbt ref syntax

   dbt uses the Jinja function ref() inside double curly braces to reference models.

2. Step 2: Match syntax with options

   SELECT * FROM {{ ref('model_name') }} uses the correct syntax: {{ ref('model_name') }} inside SQL.

3. Final Answer:

   SELECT * FROM {{ ref('model_name') }} -> Option C

4. Quick Check:

   dbt ref() = {{ ref('model') }} [OK]

Hint: Use double curly braces and ref() to reference models [OK]

Common Mistakes:

• Omitting curly braces around ref()
• Using parentheses after model name
• Using incorrect variable syntax

3. Given this dbt model SQL snippet, what will be the output columns?

SELECT user_id, COUNT(*) AS order_count
FROM {{ ref('orders') }}
GROUP BY user_id

medium

A. An error because GROUP BY is missing

B. Only user_id column without counts

C. All columns from orders table without aggregation

D. user_id and order_count columns with counts per user

Solution

1. Step 1: Analyze SELECT and GROUP BY

   The query selects user_id and counts rows grouped by user_id, so output has user_id and order_count.

2. Step 2: Confirm aggregation correctness

   GROUP BY user_id matches the aggregation COUNT(*), so query runs correctly and outputs two columns.

3. Final Answer:

   user_id and order_count columns with counts per user -> Option D

4. Quick Check:

   GROUP BY user_id + COUNT(*) = user_id, order_count [OK]

Hint: GROUP BY columns appear with aggregated counts [OK]

Common Mistakes:

• Thinking all orders columns appear
• Missing GROUP BY causes error
• Ignoring COUNT(*) aggregation

4. You wrote this dbt test to check for nulls:

SELECT * FROM {{ ref('customers') }} WHERE customer_id IS NULL

But it always fails even when nulls exist. What is the likely issue?

medium

A. The test should return zero rows to pass, but it returns rows with nulls

B. The ref function is used incorrectly without quotes

C. The test SQL syntax is invalid and causes failure

D. dbt tests cannot check for null values

Solution

1. Step 1: Understand dbt test logic

   dbt tests pass if the query returns zero rows (no errors found).

2. Step 2: Analyze test query behavior

   This query returns rows where customer_id is null, so if nulls exist, rows are returned and test fails.

3. Final Answer:

   The test should return zero rows to pass, but it returns rows with nulls -> Option A

4. Quick Check:

   dbt test passes if zero rows returned [OK]

Hint: dbt tests pass when query returns zero rows [OK]

Common Mistakes:

• Expecting test to pass when rows are returned
• Misusing ref without quotes
• Thinking dbt can't test nulls

5. In a data mesh using dbt, you want to create a shared model that combines sales data from multiple domains. Which approach best fits data mesh principles?

hard

A. Create a dbt model in a shared repository that references domain models via ref() and applies transformations

B. Copy all domain data into one big table manually outside dbt

C. Write a single SQL file that queries raw source tables directly without using refs

D. Use dbt to create separate models per domain but never combine them

Solution

1. Step 1: Understand data mesh domain ownership

   Each domain owns its data models; shared models combine them via references.

2. Step 2: Use dbt refs to combine domain models

   dbt's ref() function allows building shared models that depend on domain models cleanly.

3. Step 3: Avoid manual copying or bypassing refs

   Manual copying or querying raw tables breaks mesh principles and dbt best practices.

4. Final Answer:

   Create a dbt model in a shared repository that references domain models via ref() and applies transformations -> Option A

5. Quick Check:

   Shared model + ref() = data mesh best practice [OK]

Hint: Use shared dbt models referencing domain models with ref() [OK]

Common Mistakes:

• Copying data manually outside dbt
• Querying raw tables directly
• Not combining domain data at all