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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?

A) All of the above.
B) Raw data might be archived or deleted following a shorter retention period than production data
C) Regulatory requirements could mandate that production data be retained for longer periods.
D) Development data might have less stringent retention requirements, allowing for more frequent deletion-

2. You're setting up a new dbt project and want to customize the naming of compiled objects in the data warehouse. Which section of the dbt_project.yml file is the most relevant?

A) seeds:
B) models:
C) vars:
D) snapshots:

3. Your team decides to use separate schemas for development and production within the same data warehouse. Which of the following dbt features are essential for managing this workflow effectively?

A) Macros and Jinja templating to dynamically switch target schemas.
B) dbt snapshots to compare data state between environments-
C) dbt seeds to populate test data in your development schema.
D) Environment variables to store environment-specific configurations.

4. A dbt deployment pipeline fails on the production environment, yet everything works as intended in development and staging. Which areas would you investigate when troubleshooting?

A) Verify the credentials and connection details between production and other environments in your profiles-yml.
B) Check if all required permissions are granted to the dbt user on the production database.
C) Consider schema differences, if any between the production data warehouse and other environments.
D) Ensure consistency of dependency versions across different environments-

5. You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A) It's safe to ignore this warning, as de-duplication is handled automatically by dbt.
B) Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.
C) Increase the memory allocated for the database to handle the large number of duplicates.
D) Update the stg_customers source definition to include filtering logic to remove duplicate rows.

Solutions: