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.