(Multiple Select)

• A.Your production database has stricter data type constraints than development.
• B.Your development profile in dbt_projectyml has incorrect connection details.
• C.Environment variables used for sensitive credentials are not set correctly in production.
• D.You have accidentally granted write permissions to the production data source during testing.

Comment: *

Name: *

Email: *

Verification: *

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

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

Comment: *

Name: *

Email: *

Verification: *

Your company is adopting a "data mesh" philosophy. How might this impact how you structure and use dbt projects?

• A.dbt's role becomes less important in a data mesh architecture.
• B.Increased focus on decentralized model ownership by domain-specific teams.
• C.Stricter standardization of syntax and project structure is needed.
• D.No change in how dbt works, as it is a technology-agnostic tool.

Comment: *

Name: *

Email: *

Verification: *

You need to grant readonly access to the results of your dbt project to stakeholders outside your team. Considering security, which approach would be the MOST suitable?

• A.Create a designated reporting schema and set up views that map to the dbt model outputs
• B.Have stakeholders directly query the dbt models, providing access to the production data warehouse.
• C.Embed dbt model results within a Bl dashboard and manage access through the Bl tool.
• D.Regularly run dbt seed to export CSV files, which are then shared with stakeholders.

Comment: *

Name: *

Email: *

Verification: *

External Dependency

• A.Introduce a temporary staging model to transform the data into a compatible structure for your existing models.
• B.Implement schema tests for the affected models to catch similar breakages immediately in the future.
• C.Work with stakeholders to understand if the external change is intentional and adjust the business logic if needed.
• D.All of the above.

Comment: *

Name: *

Email: *

Verification: *