dbt Labs dbt-Analytics-Engineering Dumps Questions Answers

(new_project)

(models:)

(grants)

(require-dbt-version: '>= 1.0.0' )

The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores. Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt’s project naming rules.

To configure how models behave—materializations, schemas, tags, or per-directory overrides—dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.

When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.

Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.

dbt Python models support a limited set of materializations because they rely on execution within the data platform’s Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark). These engines require models to materialize into actual relations—tables or views—in order to persist the results of Python-based transformations.

The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:

table

view

incremental

Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.

The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.

Thus, the correct answer is No — ephemeral is not supported for Python models.

The behavior described—dbt running the orders model as a view despite being explicitly configured as a table—indicates that dbt is not correctly detecting or applying the model-level configuration during compilation. dbt relies heavily on the target-path directory to write compiled SQL, manifest files, and run artifacts. If the target-path is misconfigured, pointing to a location that dbt does not handle correctly or that overlaps with another folder used internally, dbt may fail to load the correct configuration from the merged project settings.

When dbt cannot locate the compiled configuration for a model, it defaults to its standard materialization type, which is view. This explains why the logs show:

“Finished running 1 view model”

even though the dbt_project.yml clearly declares:

materialized: table.

Additionally, the logs indicate no warnings or parsing errors, meaning dbt ran successfully but with incorrect settings—another indicator of configuration metadata being overridden or misplaced due to an incorrect target-path.

By resolving the target-path issue, dbt will successfully load the model configuration and materialize the orders model as a table as intended.