(Multiple Select)

• A.Tables generally offer the fastest query performance for pre-computed models.
• B.Tables are well-suited for datasets that undergo frequent, wholesale changes.
• C.Tables persist their results on disk, consuming storage resources.
• D.Tables, by default, are rebuilt completely with each dbt run.

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How can you overwrite configurations for models within a package?
Choose 1 option.

• A.By specifying the configurations within the packages.yml file under the relevant package entry.
• B.By adding the configurations in dbt_project.yml within the package's namespace.
• C.After installing the package, you can apply configurations on the models in the ./target folder.
• D.By navigating into the /dbt_packages folder and adding the configurations directly to the models.

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A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?

• A.Employ a snapshot of the third-party source to create a historical record.
• B.Utilize the 'defer' option to allow upstream models to run while awaiting the source update.
• C.Configure a 'view' that gracefully handles missing data from the third-party source.
• D.Set up an alert to notify users when the upstream data source is not refreshed on time.

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Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

• A.Views cannot perform complex date manipulation required for YoY calculations.
• B.Data warehouses might not be optimized for period-over-period comparisons.
• C.ref() functions can hinder performance when working with historical data.
• D.Incremental materializations aren't suitable for trend analysis.

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You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

• A.Close the original pull request and create a new branch with the revised implementation.
• B.Directly modify the commits on your existing feature branch using git rebase -i.
• C.Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
• D.Request that your collaborators edit the code directly within the pull request interface.

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