A new feature you're developing requires significant changes across multiple models in your dbt project. Which branching strategy would be the most suitable and why?

• A.Work directly on the main branch, as it ensures your changes are always up-to-date.
• B.Create a feature branch for the new development to isolate your work.
• C.Create separate branches for each model you'll be modifying to minimize conflicts.
• D.Avoid using branches altogether, as they can introduce unnecessary complexity into a dbt workflow

Comment: *

Name: *

Email: *

Verification: *

(Multiple Select)

• A.Macros for fiscal period calculations or string manipulations.
• B.Models addressing a domain-specific use case (e.g., subscription analytics).
• C.Snapshots that track the state of your sources on a schedule.
• D.A custom adapter to connect to a niche data source.

Comment: *

Name: *

Email: *

Verification: *

During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?

• A.Use development environment settings that incrementally build models only when code changes.
• B.Strategically employ ephemeral models in development to avoid materializing intermediate data repeatedly.
• C.Temporarily decrease the sample size of data during development, while reverting to full data processing in other environments.
• D.All of the above.

Comment: *

Name: *

Email: *

Verification: *

A developer imports a package from a private repository called timeformat for use within their project.
Which statement is correct? Choose 1 option.
Options:

• A."The package can be added with this configuration in the packages.yml file:" packages:
  - local: /opt/dbt/timeformat
• B."The package can be installed by running the command dbt build."
• C."The package default schema can be overridden in the dbt_project.yml file as:" models:
  timeformat:
  +schema: timeseries
• D."Including the package version/revision in the packages.yml file, for private git packages will result in an error."

Comment: *

Name: *

Email: *

Verification: *

Performance Bottleneck

• A.Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
• B.Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
• C.Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
• D.Implement database-level indexes on columns involved in the joins, if appropriate.

Comment: *

Name: *

Email: *

Verification: *