You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

• A.dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
• B.Retrieving runtime metrics for each historical run might not be natively supported by dbt
• C.Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
• D.All of the above

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You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?

• A.Create a pre-hook that automates schema validation and sends the output to the designated team.
• B.Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.
• C.Write dbt tests that explicitly fail if the new source schema does not match the approved definition.
• D.Use dbfs ephemeral models feature to temporarily load the data for the approval process.

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(Multiple Select)

• A.Use dbt tags to group models based on update frequency
• B.Configure separate dbt jobs with different schedules tailored to the model groups.
• C.Employ dbt snapshots to selectively run only models that depend on changed sources.
• D.Have all models run nightly but use where conditions within models to limit work when data is unchanged.

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Which is true about writing generic tests?
Choose 1 option.

• A.They should accept one or both of these arguments: model and column_name.
• B.They must contain a {{ ref() }} snippet to a model.
• C.They do not need to be specified under a model's YAML configurations.
• D.They should always accept a column_name.
• E.They are written using the {% macro %} wrapper.

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