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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Availability of specific dbt packages used in your project.
B) The target database schema where models are materialized.
C) The specific SQL dialect used when generating model code.
D) Credentials used to authenticate to the data warehouse.

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

3. (Multiple Select)

A) Data type conversions in your models consistently fail during execution.
B) You encounter an error message that references a specific line number in the compiled code.
C) You're trying to optimize the performance of a particularly complex transformation.
D) You suspect that filters in a source definition arent being applied correctly.

4. (Multiple Select)

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

5. A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?

A) dbt offers native support for advanced scheduling and orchestration features.
B) dbt models are typically more performant than hand-written SQL scripts.
C) dbt promotes modularity, reusable code, and testing within data transformation pipelines.
D) dbt automatically generates user-friendly data lineage visualizations.

Solutions: