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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You encounter a runtime error: "Column 'order_date' not found in the target relation". You examine your model and see:SQL

A) Ensure there are no downstream models that could be altering the 'fct_orders' table.
B) Verify that your Jinja templating in other models isn't interfering with column names.
C) Review the fct_orders model's SQL logic to see if it unintentionally removes or renames the 'order_date' column.
D) Check if the fct_orders model or source actually includes an 'order_date' column.

2. A large dimension table is joined frequently in your dbt models. Due to its size, these joins significantly impact performance. Which strategy might offer improvement?

A) Add a WHERE clause to the model to filter out unnecessary rows during the join.
B) Rewrite the join logic by nesting conditions within a CTE.
C) If the dimension data rarely changes, convert it to an incremental model.
D) Investigate database-specific techniques like hash joins or distribution keys for optimizing the join.

3. You must exclude a specific model from being included in the generated documentation entirely. How would you achieve this?

A) Remove all descriptions and tags associated with the model.
B) Add a dæs: {enabled: false} entry to the model's YAML configuration.
C) Its not possible to exclude individual models; you can only exclude entire folders.
D) Use the enabled flag in your dbt_projectyml file to control model inclusion.

4. You're experimenting with new transformations. You want to iterate on a model without affecting data in the production warehouse. Which dbt command should you use along with appropriate flags?

A) dbt test with -defer to temporarily bypass upstream dependencies.
B) dbt compile to ensure changes are valid without executing models.
C) dbt run with -target to specify a development environment.
D) dbt seed with -select to load data into production.

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

Solutions: