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1. You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?
A) Modify the macro to only retrieve sample values during development and not for production documentation.
B) Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
C) Increase the compute resources allocated to your dbt documentation generation process.
D) Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.
2. A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?
A) Incremental materializations will help verify that the changes are successful.
B) With clear dependencies, they can pinpoint which upstream models to test.
C) By using macros, modifications are limited to a single location.
D) Snapshots ensure that historical data won't be impacted unintentionally.
3. You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?
A) Analyze commonalities and differences in the transformations to determine refactoring potential.
B) Test individual models thoroughly to establish a baseline before making changes.
C) Create CTEs to segment logical steps within the existing model SQL.
D) Use a text editor's "find and replace" function to update repetitive code blocks.
4. You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?
A) The need to collaborate between developers with varying skill levels.
B) Your data warehouse's pricing model and how it charges for resource usage.
C) The complexity of your dbt project and the number of models.
D) The desired level of isolation and access control granularity.
5. Type Mismatch
A) Handle the type conversion with a custom macro and apply it selectively within your dbt project.
B) Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
C) Introduce a staging model specifically to perform the type conversion, creating a clean separation.
D) Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
Solutions:
| Question # 1 Answer: A,B,D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: A,B,D | Question # 5 Answer: C |
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