(Multiple Select)

• A.You detect a frequently-used, non-trivial SQL transformation pattern across multiple models.
• B.You need to perform data type conversions for a specific set of columns.
• C.Your data warehouse has a non-standard SQL function used in various reporting dashboards.
• D.You have a very simple filter that is applied to just a small subset of models.

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Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

• A.Write the seed data into log files, excluding them from version control.
• B.Utilize environment variables to store seed data values.
• C.Create separate seed files and add them to your .gitignore or equivalent.
• D.Employ a secrets management tool and integrate it with your dbt deployment.

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(Multiple Select)

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

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Which of the following is true about restricting the usage of models in dbt?
Choose 1 option.

• A.Restrictions are set by defining the models and Git user groups approved for usage.
• B.You must map your data platform roles to groups by specifying the role name in the configurations.
• C.Model groups can limit references by other models which aren't in the same group.
• D.Data platform user emails can be used to determine who can reference a model.

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Data Quality Issue

• A.Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
• B.Run dbt test on the model and see if existing tests fail, indicating a possible regression.
• C.Add specific schema tests to the model, asserting that the column in question should not contain nulls.
• D.Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.

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